Energy system configuration optimization method considering variable working condition characteristics of equipment and energy storage life attenuation

Abstract

The invention relates to an energy system configuration optimization method considering equipment variable working condition characteristics and energy storage life attenuation, which comprises the following steps: establishing a mathematical model of an energy system, and calculating equipment cycle aging indexes in the mathematical model of power storage equipment by considering the energy storage life attenuation, in mathematical models of the cold-heat-electricity combined supply system, the gas-fired boiler and the electric refrigerating unit, the corresponding efficiency is calculated by considering the variable working condition characteristics of the equipment; and based on the established mathematical model, resource configuration optimization of the energy system is carried out by using a double-layer optimization method, the upper layer optimizes equipment capacity design by taking minimization of annual cost as a target, and the lower layer carries out operation optimization by taking the sum of minimization of operation cost, battery life degradation cost and demand electricity price as a target. The battery life degradation cost is calculated based on the equipment cycle aging index. Compared with the prior art, battery life attenuation and equipment variable working condition operation are considered at the same time, and the overall efficiency and reliability of the system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of optimal configuration of energy systems, and in particular to an optimal configuration method for an energy system considering the variable operating conditions of equipment and the life attenuation of energy storage. Background Art

[0002] The continuous increase in energy consumption has led to the exacerbation of the energy crisis and the deterioration of the greenhouse effect. More efficient energy systems and the efficient utilization of renewable energy are considered effective solutions. As a typical form of an integrated energy system (IES), the combined cooling, heating, and power (CCHP) system is known for its high efficiency, cleanliness, economy, and reliability. Facing diverse user demands, unstable renewable energy, and fluctuating load demands, the reasonable design of the integration of the CCHP system and the adjustment of its variable operating conditions are crucial for improving system performance. The supply-demand matching between the system and users plays a key role in determining the applicability of system integration and operation. This matching directly affects the operating efficiency of the system and the energy relationship between the system and users, and further affects the energy-saving potential and applicability of the system. The coupling of an energy storage system (ESS) with a combined heat and power (CCHP) system is an effective strategy for balancing energy supply and demand and ensuring system performance and reliability. The main purpose of the ESS is to separate the generation of heat and electricity by storing excess energy that would otherwise be wasted and reuse it when needed. A well-designed ESS can reduce or eliminate the need for auxiliary equipment and allow the production system to operate at higher temperatures and more efficient loads, thus saving energy, reducing carbon dioxide emissions, and lowering costs.

[0003] It is known that both the life attenuation of the energy storage system and the variable operating conditions of equipment will have a certain impact on the capacity configuration results of the integrated energy system. Ignoring the impact of battery life attenuation and variable operating conditions in the energy system may lead to the following problems:

[0004] 1) Underestimating or overestimating the configuration and operating costs of the system;

[0005] 2) Ignoring the function of the energy storage system;

[0006] 3) A large deviation occurs between the capacity configuration results of system equipment and the actual required results.

[0007] CN115496306A discloses an energy system full - life - cycle capacity configuration method, device, and storage medium, which are applicable to distributed energy systems. The method includes: calculating parameters of the distributed energy system according to its structure; constructing a mathematical model for optimal capacity configuration of the full - life - cycle cost of the distributed energy system based on decision variables, objective functions, constraint conditions, and parameters, considering the variable operating conditions of equipment; and solving the mathematical model for optimal capacity configuration of the full - life - cycle cost to obtain the optimal capacity configuration when the full - life - cycle cost converges to the minimum value. Although this method considers the influence of different operating conditions, it does not reflect the life attenuation of energy storage devices. Moreover, with the reform of the electricity price system, the popularity of two - part tariffs has further increased, but the existing methods lack consideration of two - part tariffs. Summary of the Invention

[0008] The purpose of the present invention is to provide an energy system configuration optimization method considering the variable operating conditions of equipment and the life attenuation of energy storage, so as to realize the optimization of the CCHP system coupled with EES, considering the battery life attenuation and the variable operating conditions of equipment, and at the same time incorporating the demand charge of the two - part tariff as an optimization target into the economic optimization model of the energy system to improve the overall efficiency and reliability of the system.

[0009] The purpose of the present invention can be achieved through the following technical solutions:

[0010] An energy system configuration optimization method considering the variable operating conditions of equipment and the life attenuation of energy storage includes the following steps:

[0011] Establish a mathematical model of the energy system. The energy system includes a photovoltaic system, a combined cooling, heating, and power (CCHP) system, a gas boiler, an electric refrigeration unit, an energy storage device, and a cold and heat storage device. Among them, in the mathematical model of the energy storage device, the energy storage life attenuation is considered to calculate the equipment cycle aging index, and in the mathematical models of the CCHP system, the gas boiler, and the electric refrigeration unit, the variable operating conditions of the equipment are considered to calculate the corresponding efficiency.

[0012] Based on the established mathematical model, use a two - layer optimization method to optimize the resource configuration of the energy system. Among them, the upper layer of the two - layer optimization method aims to optimize the equipment capacity design with the goal of minimizing the annualized cost, and the lower layer aims to optimize the operation with the goal of minimizing the sum of the operation cost, the battery life degradation cost, and the demand charge, where the battery life degradation cost is calculated based on the equipment cycle aging index.

[0013] The mathematical model of the photovoltaic system is:

[0014] P pv =A pv P pv,s

[0015] A pv = γA roof

[0016] where P pv,s is the power generation of the photovoltaic panel per unit area, A pv is the photovoltaic area, A roof is the roof area where photovoltaic can be installed, γ is the ratio of the area where photovoltaic can be installed, and P pv is the power generation of photovoltaic power generation.

[0017] The combined cooling, heating and power supply system includes a gas turbine, a waste heat boiler and an absorption chiller, where

[0018] The mathematical model of the gas turbine is:

[0019] P GT,e = η GT,e F GT L NG

[0020] where P GT,e is the power generation power of the gas turbine, η GT,e is the power generation efficiency of the gas turbine, F GT is the gas consumption, L NG is the calorific value of natural gas. Among them, the power generation efficiency of the gas turbine is determined considering the variable operating conditions of the equipment:

[0021] η GT = a1 × PLR GT 2 + b1 × PLR GT + c1

[0022]

[0023] where a1, b1, c1 are electrical efficiency coefficients, PLR GT is the partial load ratio of the gas turbine, and CAP GT is the rated capacity of the gas turbine;

[0024] The mathematical model of the waste heat boiler is:

[0025] Q WHB,h = η GT,h F GT L NG

[0026] where Q WHB,h is the heat production of the waste heat boiler, η GT,h is the thermal efficiency of the gas turbine, which is determined considering the variable operating conditions of the equipment:

[0027] η GT,h = a2 × PLR GT 2+b2 × PLR GT +c2

[0028] Wherein, a2, b2, and c2 are the gas turbine thermal efficiency coefficients;

[0029] The mathematical model of the absorption chiller is:

[0030] L AC,c = Q AC,h η AC

[0031] Wherein, L AC,c is the cooling capacity of the absorption chiller, Q AC,h is the heat input to the absorption chiller, and η AC is the efficiency of the absorption chiller.

[0032] The mathematical model of the gas boiler is:

[0033] Q GB = η GB F GB L NG

[0034] Wherein, Q GB is the heat output of the gas boiler, F GB is the gas consumption of the gas boiler, L NG is the calorific value of natural gas, and η GB is the boiler efficiency, which is determined considering the variable operating conditions of the equipment:

[0035] η GB = a3 × PLR GB 2 + b3 × PLR GB + c3

[0036]

[0037] Wherein, a3, b3, and c3 are the gas boiler thermal efficiency coefficients, CAP GB is the rated capacity of the gas boiler, and PLR GB is the part load ratio of the gas boiler.

[0038] The mathematical model of the electric refrigeration unit is:

[0039] L EC = P EC,in EER EC

[0040] Wherein, L EC is the cooling output of the electric refrigeration unit, P EC,in is the power consumption of the electric refrigeration unit, and EER EC is the energy efficiency ratio of the unit, which is determined considering the variable operating conditions of the equipment:

[0041] EER EC = a4 × PLR EC 2 + b4 × PLR EC + c4

[0042]

[0043] where a4, b4, and c4 are refrigeration efficiency coefficients, and PLR EC is the partial load ratio of the electric refrigeration unit.

[0044] The mathematical model of the energy storage device is:

[0045]

[0046] where BES(t) is the battery charge at time t, and are the battery charge and discharge efficiencies respectively, and are the battery charge and discharge powers respectively, Δt is the time interval, and σ BES is the battery attenuation coefficient;

[0047] Considering the influence of cyclic aging, calculate the device cyclic aging index:

[0048]

[0049] where EFC is the total energy provided by a complete charge and discharge cycle of the energy storage system, and CAP BES is the capacity of the energy storage device, is the number of cycles when the battery health SOH = 80%.

[0050] The mathematical model of the cool and heat energy storage device is:

[0051]

[0052] where T / CES(t) is the capacity of the cool energy storage device at time t, and σ T / CES is the self-heat release coefficient, and are the cool and heat release efficiencies respectively, and are the cool and heat release amounts respectively, and Δt is the time interval.

[0053] The optimization objective of the upper layer of the two-layer optimization method is to minimize the annualized cost C total :

[0054] C total = C fac + C om + Clower

[0055]

[0056] Among them, C fac , C om , C lower are the equipment investment cost, operation and maintenance cost, and the operation cost of the lower - layer calculation respectively. CRF k is the capacity recovery factor of equipment k, CAP k is the equipment capacity of equipment k, f k,buy is the unit cost of equipment k, f k,om is the unit operation and maintenance cost of equipment k, r is the annual interest rate, l k is the service life of equipment k;

[0057] The upper - layer constraint conditions include the rated capacity constraint of each device.

[0058] The optimization objective of the lower - layer is to minimize C lower :

[0059] C lower = C op + C bd + C cp

[0060]

[0061] Among them, C op , C bd , C cp are the operation cost, battery life degradation cost, and demand electricity price respectively. β ele,buy,t is the unit power purchase cost, T is the time, P grid,buy (t) is the grid power purchase volume, β gas is the gas purchase cost, F GT is the gas consumption of the gas turbine, F GB is the gas consumption of the gas boiler, PVF is the net present value, κ is the maximum allowable capacity decay percentage at the end of the battery life, AGE cyc (t) is the cycle life, is the battery price in the l k th year, i is the interest rate, l k is the service life of equipment k, υ is the unit demand electricity price, P buy is the power purchase volume, n is the number of months;

[0062] The lower - layer constraint conditions include: energy balance constraint, equipment operation constraint, and energy storage operation constraint.

[0063] The two - layer optimization method uses a genetic algorithm to solve in the upper - layer and an interior - point optimizer to solve in the lower - layer, including the following steps:

[0064] S1. Obtain basic parameters and conditions, including: genetic algorithm parameters, investment cost per unit equipment capacity, equipment operation and maintenance cost, and maximum equipment capacity limit;

[0065] S2. Encode the upper-layer decision variables using the genetic algorithm;

[0066] S3. Generate an initial population;

[0067] S4. Use an interior point optimizer to perform lower-layer optimization and solution to obtain the hourly output power of the equipment;

[0068] S5. Calculate the individual fitness value based on the solution results of the lower layer;

[0069] S6. Determine whether the convergence condition is satisfied. If so, output the final optimization result; otherwise, generate a new population and perform selection, crossover, and mutation operations based on the individual fitness value, and return to step S4 for optimization and solution.

[0070] Compared with the prior art, the present invention has the following beneficial effects:

[0071] (1) In the established mathematical model of the present invention, the influence of variable operating conditions of the equipment is considered, which can accurately describe the influence of the change of cooling, heating, and power loads over time on the system efficiency and operating cost, thereby improving the dynamic adaptability and practical feasibility of the configuration optimization.

[0072] (2) The present invention considers the battery life attenuation. By introducing a battery life attenuation cost model, it realizes the collaborative optimization of the design and operation of the energy system considering battery life attenuation, more realistically evaluates the operating cost of the energy storage system, and extends the system life cycle.

[0073] (3) The present invention introduces the demand tariff in the two-part tariff model as part of the optimization objective, realizes the precise control of the maximum power load, effectively reduces the demand tariff expenditure brought by the peak electricity consumption, and enhances the system's response ability to the electricity price structure. Description of the Drawings

[0074] Figure 1 is the overall flowchart of the energy system configuration optimization method of the present invention;

[0075] Figure 2 is the energy system architecture diagram in an embodiment;

[0076] Figure 3 is the schematic diagram of the solution process of the two-layer optimization method in an embodiment;

[0077] Figure 4Schematic diagram of cooling, heating and power loads in an embodiment, where (a) is a hotel building, (b) is an office building, (c) is a residential building, and (d) is a commercial building;

[0078] Figure 5 Schematic diagram of energy prices in an embodiment;

[0079] Figure 6 Cost comparison chart of different cases in an embodiment;

[0080] Figure 7 Schematic diagram of the annual load bearing ratio of IES in an embodiment;

[0081] Figure 8 Schematic diagram of the system scheduling result in an embodiment, where (a) is the power balance, (b) is the heat balance, and (c) is the cold balance. Detailed implementation mode

[0082] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. This embodiment is implemented on the premise of the technical solution of the present invention, and detailed implementation methods and specific operation processes are given, but the protection scope of the present invention is not limited to the following embodiments.

[0083] This embodiment provides an energy system configuration optimization method considering the variable operating conditions of equipment and the degradation of energy storage life, including the following steps:

[0084] Step 1) Establish a mathematical model of the energy system.

[0085] As Figure 2 shown, the energy system includes a photovoltaic system PV, a combined cooling, heating and power system CCHP, a gas boiler GB, an electric refrigeration unit EC, a battery energy storage device BES, and a thermal energy storage device T / CES. The combined cooling, heating and power system CCHP includes an absorption refrigeration unit AC, a waste heat boiler WHB, and a gas turbine GT.

[0086] 1. Photovoltaic system PV

[0087] The photovoltaic system is used to convert solar radiation into electrical energy. The photovoltaic array is composed of many photovoltaic modules connected in parallel and can provide clean energy for the area. The power generation of the photovoltaic array is described by formula (1) and formula (2).

[0088] P pv =A pv P pv,s (1)

[0089] A pv =γA roof (2)

[0090] Among them, P pv,sThe power generation of the photovoltaic panel per unit area, A pv The photovoltaic area, A roof The roof area where photovoltaic can be installed, γ is the ratio of the area where photovoltaic can be installed, P pv The power generation of photovoltaic power generation.

[0091] 2. Combined Cooling, Heating and Power System CCHP

[0092] The combined cooling, heating and power (CCHP) system integrates a gas turbine (GT), a waste heat boiler (WHB) and an absorption chiller (AC).

[0093] 21. Gas Turbine GT

[0094] The gas turbine generates waste heat while generating electricity, and this waste heat is efficiently captured by the waste heat boiler. Subsequently, this waste heat is utilized by the absorption chiller to meet the heating and cooling demands of the system, thereby improving the overall energy efficiency. The corresponding mathematical model is shown in formulas (3) to (5).

[0095] P GT,e = η GT,e F GT L NG (3)

[0096] Among them, P GT,e is the power generation power of the gas turbine, η GT,e is the power generation efficiency of the gas turbine, F GT is the gas consumption, L NG is the calorific value of natural gas. Among them, the power generation efficiency η of the gas turbine GT,e is determined considering the off-design characteristics of the equipment:

[0097] η GT = a1 × PLR GT 2 + b1 × PLR GT + c1 (4)

[0098]

[0099] Among them, a1, b1, c1 are the electrical efficiency coefficients, and usually specific values obtained by fitting through experimental data or performance curves provided by the manufacturer. PLR GT is the partial load ratio of the gas turbine, CAP GT is the rated capacity of the gas turbine.

[0100] 22. Waste Heat Boiler WHB

[0101] The mathematical model of the waste heat boiler is:

[0102] Q WHB,h = η GT,hF GT L NG (6)

[0103] Among them, Q WHB,h is the heat output of the waste heat boiler, and η GT,h is the thermal efficiency of the gas turbine, which is determined considering the off-design characteristics of the equipment:

[0104] η GT,h = a2 × PLR GT 2 + b2 × PLR GT + c2 (7)

[0105] Among them, a2, b2, and c2 are the thermal efficiency coefficients of the gas turbine.

[0106] 23. Absorption chiller AC

[0107] The mathematical model of the absorption chiller is:

[0108] L AC,c = Q AC,h η AC (8)

[0109] Among them, L AC,c is the cooling capacity of the absorption chiller, Q AC,h is the heat input to the absorption chiller, and η AC is the efficiency of the absorption chiller.

[0110] 3. Gas boiler GB

[0111] The mathematical model of the gas boiler is:

[0112] Q GB = η GB F GB L NG (9)

[0113] Among them, Q GB is the heat output of the gas boiler, F GB is the gas consumption of the gas boiler, L NG is the calorific value of natural gas, and η GB is the boiler efficiency, which is determined considering the off-design characteristics of the equipment:

[0114] η GB = a3 × PLR GB 2 + b3 × PLR GB + c3 (10)

[0115]

[0116] Among them, a3, b3, and c3 are the thermal efficiency coefficients of the gas boiler, CAPGB is the rated capacity of the gas boiler, PLR GB is the partial load ratio of the gas boiler.

[0117] 4. Electric chiller unit EC

[0118] The electric chiller (EC) is used to meet the cooling load during peak hours and store cold energy in the thermal energy storage device during off-peak hours. Its mathematical model is described by Equation (12).

[0119] L EC = P EC,in EER EC (12)

[0120] where L EC is the cooling capacity output by the electric chiller unit, P EC,in is the power consumption of the electric chiller unit, and EER EC is the energy efficiency ratio of the unit, which is determined considering the variable operating conditions of the equipment:

[0121] EER EC = a4 × PLR EC 2 + b4 × PLR EC + c4 (13)

[0122]

[0123] where a4, b4, and c4 are the refrigeration efficiency coefficients, and PLR EC is the partial load ratio of the electric chiller unit.

[0124] 5. Energy storage equipment

[0125] The energy storage equipment (energy storage device) selected in this embodiment is a lithium-ion battery. The battery energy storage system (BES) can store electrical energy from photovoltaic (PV), gas turbine (GT), and purchased from the grid during off-peak hours and then supply it to the community. Equation (15) represents the energy storage capacity at time t + 1.

[0126]

[0127] where BES(t) is the battery charge at time t, and are the battery charge and discharge efficiencies respectively, and are the battery charge and discharge powers respectively, Δt is the time interval, and σ BES is the battery attenuation coefficient.

[0128] Typically, the initial State of Health (SOH) is set to 100%, while the SOH at the end of life is 80%. The degradation of the battery life is affected by both calendar aging and cycle aging. However, since the impact of cycle aging is much greater than that of calendar aging, this embodiment only focuses on the impact of cycle aging.

[0129] The Equivalent Full Cycle (EFC) is the total energy provided, representing a complete charge-discharge cycle of the energy storage system. The EFC can be estimated using Equation (16). The cycle life is the maximum number of complete charge-discharge cycles that can be achieved under specific conditions. Therefore, the cycle aging of the energy storage system is quantified as the ratio of the EFC to the cycle life, which is the device cycle aging index, as shown in Equation (17).

[0130]

[0131] where EFC is the total energy provided for a complete charge-discharge cycle of the energy storage system, CAP BES is the capacity of the electricity storage device, is the number of cycles when the battery health SOH = 80%.

[0132] 6. Cold and heat storage equipment

[0133] The mathematical model of the cold and heat storage equipment is:

[0134]

[0135] where T / CES(t) is the capacity of the cold storage equipment at time t, σ T / CES is the self-heat release coefficient, and are the heat storage and release efficiencies respectively, and are the heat storage and release amounts respectively, and Δt is the time interval.

[0136] Step 2) Based on the established mathematical model, use the two-layer optimization method to optimize the resource allocation of the energy system.

[0137] As Figure 1 shown, this embodiment proposes a two-layer optimization method to ensure the stability of the solution process. Among them, the upper layer optimizes the device capacity design with the goal of minimizing the annualized cost, and uses the Genetic Algorithm (GA) for optimization and solution. The lower layer optimizes the operation with the goal of minimizing the sum of the operation cost, the battery life degradation cost, and the demand electricity price, and uses the Interior Point Optimizer (IPOPT) solver for solution.

[0138] The optimization goal of the upper layer of the two-layer optimization method is to minimize the annualized cost C total :

[0139] C total = Cfac +C om +C lower (19)

[0140]

[0141] Among them, C fac , C om , C lower are the equipment investment cost, operation and maintenance cost, and operation cost of the lower-layer calculation respectively. CRF k is the capacity recovery factor of equipment k, CAP k is the equipment capacity of equipment k, f k,buy is the unit cost of equipment k, f k,om is the unit operation and maintenance cost of equipment k, r is the annual interest rate, l k is the service life of equipment k.

[0142] The upper-layer constraint conditions include the rated capacity constraint of each device:

[0143] 0 ≤ CAP k ≤ CAP k,max (23)

[0144] Among them, CAP k,max is the maximum equipment rated capacity of equipment k.

[0145] The upper-layer decision variables include the rated capacity of the gas turbine CAP GT , the rated capacity of the waste heat boiler CAP WHB , the rated capacity of the absorption chiller CAP AC , the rated capacity of the gas boiler CAP GB , the rated capacity of the electric refrigeration unit CAP EC , the rated capacity of the energy storage device CAP BES , the rated capacity of the cold and heat storage device CAP T / CES .

[0146] The optimization objective of the lower layer is to minimize C lower :

[0147] C lower = C op + C bd + C cp (24)

[0148]

[0149]

[0150] Among them, C op , C bd , C cpThey are the operating cost, the battery life degradation cost, and the demand electricity price, respectively. β ele,buy,t is the unit power purchase cost, T is the time, and P grid,buy (t) is the grid power purchase quantity, β gas is the gas purchase cost, F GT is the gas consumption, F GB is the gas consumption of the gas boiler, PVF is the net present value, κ is the maximum allowable capacity attenuation percentage at the end of the battery life, and AGE cyc (t) is the cycle life, is the battery price in the l k -th year, i is the interest rate, and l k is the service life of equipment k, υ is the unit demand electricity price, and P buy is the power purchase quantity, and n is the number of months;

[0151] The constraints of the lower layer include:

[0152] a. Energy balance constraint:

[0153]

[0154] where P load , Q load , and L load are the cooling, heating, and electricity loads respectively, P grid,buy is the power purchase quantity, P aba is the curtailed power quantity, Q GB is the heat output of the gas boiler, Q WHB is the heat output of the waste heat boiler, is the heat release / accumulation quantity of the heat / cold storage equipment, Q AC,h is the heat consumption of the absorption chiller, L AC is the cooling capacity of the absorption chiller, L eC is the cooling capacity of the electric chiller, is the heat release / cooling storage quantity of the heat / cold storage equipment.

[0155] b. Equipment operation constraints:

[0156] 0.2CAP GT ≤P GT,e ≤CAP GT (32)

[0157] 0≤Q WHB,h ≤CAP WHB (33)

[0158] 0≤L AC,c ≤CAP AC (34)

[0159] 0≤Q GB ≤CAP GB(35)

[0160] 0.2CAP EC ≤L EC ≤CAP EC (36)

[0161] Among them, Q WHB,h is the heat production of the waste heat boiler, and L AC,c is the refrigerating capacity of the absorption refrigeration unit.

[0162] c. Energy storage operation constraint:

[0163] SOC BES,min CAP BES ≤BES≤SOC BES,max CAP BES (37)

[0164]

[0165] BES(1) = BES(end) (40)

[0166] SOC T / CES,min CAP T / CES ≤T / CES≤SOC T / CES,max CAP T / CES (41)

[0167]

[0168]

[0169] Among them, SOC BeS,max , SOC BES,min are the maximum and minimum SOC of the electricity storage device, is the maximum and minimum charge-discharge power, BES(1) and BES(end) are the initial and final capacity states of BES within a day, and SOC T / CES,max , SOC T / CES,min are the maximum and minimum SOC of T / CES, is the maximum and minimum heat storage and release power, and T / CES(1) and T / CES(end) are the initial and final capacity states of T / CES within a day.

[0170] As Figure 3 shown, the solution of the two-layer optimization method includes the following steps:

[0171] S1. Obtain the basic parameters and conditions, including: genetic algorithm parameters, unit device capacity investment cost, equipment operation and maintenance cost, and maximum device capacity limit;

[0172] S2. Encode the upper-layer decision variables using the genetic algorithm;

[0173] S3. Generate an initial population;

[0174] S4. Use an interior point optimizer to perform lower - layer optimization and solution to obtain the hourly output power of the device;

[0175] S5. Calculate the individual fitness value based on the solution result of the lower layer;

[0176] S6. Determine whether the convergence condition is met. If so, output the final optimization result; otherwise, generate a new population and perform selection, crossover, and mutation operations based on the individual fitness value, and return to step S4 for optimization and solution.

[0177] In addition to optimizing the economy of the system, this embodiment also introduces carbon emissions and primary energy utilization efficiency as evaluation indicators to analyze the environmental pollution impact and energy efficiency of the system.

[0178]

[0179] Among them, are the carbon emission factors for gas and electricity respectively, n grid is the power generation efficiency of the power plant, which is 0.45 in this embodiment, Q gas is the calorific value of natural gas, which is 9.7 kWh / m³ in this embodiment, P pv,use (t) is the power generation from photovoltaic (PV).

[0180] This embodiment also provides an example to elaborate on the above - mentioned method in detail. The cooling, heating, and power loads of the research object are as Figure 4 shown, the energy prices are as Figure 5 shown, and the remaining technical parameters are shown in Table 1 below.

[0181] Table 1

[0182]

[0183]

[0184]

[0185] To emphasize the importance of considering battery life degradation and the variable load characteristics of the device, this embodiment analyzes four different scenarios: Case 1: Do not consider battery life degradation and the variable load characteristics of the device; Case 2: Only consider battery life degradation; Case 3: Only consider the variable load characteristics of the device; Case 4: Consider both battery life degradation and the variable load characteristics of the device. The optimal configuration results for each case are shown in Table 2, and the relevant costs are as Figure 6 shown.

[0186] Comparing Case 1 and Case 2, it is obvious that the battery capacity without considering battery life degradation is 893.09 kWh, which is significantly higher than the capacity after considering degradation (101.53 kWh). This difference occurs because the cost of the energy storage device is underestimated. As a result, when the peak-valley electricity price difference exceeds the levelized cost of energy (LCOE) of the battery configuration, the system tends to overconfigure the energy storage device in order to obtain greater profits by taking advantage of the price difference. However, when including the battery life degradation cost (such as in Case 2), the capacity of the configured energy storage device decreases significantly. This is because the degradation of the battery significantly increases the overall system cost, thus affecting its economic feasibility. The overestimation of the energy storage capacity leads to the primary energy supply of the system tending to electricity, increasing the peak-valley arbitrage electricity volume and reducing the demand for gas turbine power generation. Therefore, the demand for GT (gas turbine), WHB (hot water boiler), and AC (absorption chiller) in Case 1 decreases. In addition, the underestimation of the capacities of GT, AC, and WHB leads to the overestimation of the capacity of the T / CES (thermal / cold energy storage system).

[0187] Comparing Case 1 and Case 3, it can be observed that when considering the variable operating characteristics of the equipment, the capacities of all equipment decrease, while the capacity of the electric chiller increases. This adjustment is due to the expansion of the system's thermoelectric ratio range, which in turn improves the overall flexibility of the system. By allowing the equipment to operate more efficiently under a wider range of conditions, the system can better adapt to different demands, thus reducing the need for excess capacity in most components. In addition, the peak efficiency of the equipment usually exceeds its rated efficiency. This is because the variable operating characteristics enable the equipment to operate closer to its optimal efficiency point more frequently, thereby improving performance and saving energy.

[0188] Comparing Case 3 and Case 4, it can be observed that after incorporating the battery life degradation cost (Case 4), the capacity configuration of the energy storage device decreases significantly, and the system is more inclined to rely on gas, increasing the capacities of GT, WHB, and AC, while the capacity of the EC (electric chiller) decreases.

[0189] From Figure 6 it can be seen that ignoring the battery life degradation cost can reduce the system operation cost by 18%. However, the objective existence of the energy storage life degradation cannot be ignored. This method significantly increases the system capacity configuration cost and the battery life degradation cost, resulting in a 2.8% increase in the total system cost. When considering the variable operating characteristics of the system equipment, the flexibility and peak efficiency of the system increase, leading to a 6.5% reduction in the total cost. In addition, comparing Case 1 and Case 4, it is obvious that considering both the variable operating characteristics of the system equipment and the battery life degradation cost, the total cost is reduced by 8.3% to reach 79,266,409.44 yuan.

[0190] Table 2

[0191] BES GT WHB AC EC GB T / CES Case1 893.09 99.86 116.67 140.01 334.76 100.17 1206.00 Case2 101.53 119.83 140.00 168.01 326.31 96.77 894.23 Case3 525.42 91.48 104.15 124.97 422.20 91.53 726.04 Case4 143.96 121.08 137.85 165.42 375.56 87.77 739.55

[0192] The optimization results of the integrated energy system (IES) are shown in Case 4 of Table 2, and the annual load-carrying ratio of the IES is as Figure 7 shown. The total energy output of the system comes from the CCHP (combined cooling, heating, and power) system, which is responsible for 72% of the electrical load, 52% of the heating load, and 36% of the cooling load. Through the complementary use of multiple energy forms, the energy utilization rate of the system is significantly improved. In addition, photovoltaic (PV) contributes 9% of the annual electrical load. This relatively small contribution can be attributed to the limitations imposed by the limited roof space, which restricts the potential installation capacity of PV panels in the urban environment.

[0193] As Figure 8 shown in the daily energy supply pattern, it highlights the strategic operation of the system in winter. Although the electricity price is low during off-peak hours, the heating load of the system requires the heat-driven operation of the gas turbine (GT). In this mode, the GT mainly generates electricity to meet the heat demand, and any surplus electricity is effectively stored in the battery energy storage system for discharging during the daytime peak electricity price period. At the same time, the thermal / cooling energy storage system (T / CES) pre-stores a certain amount of heat. During peak heating periods, the hot water boiler (WHB), gas boiler (GB), and T / CES jointly provide heat, effectively reducing the required installed capacity of the GB and CCHP systems.

[0194] In the transitional season, the operation mode is similar to that in winter. The thermal energy storage device pre-stores a portion of heat for daytime use, while the energy storage device stores electricity for use during peak pricing periods. The presence of the energy storage system enables the entire system to maintain a relatively efficient operating state. During this period, the cooling load of the system mainly comes from the absorption chiller (AC).

[0195] In summer, the T / CES stores a certain amount of cold energy at night for daytime use. The cold energy during peak hours is mainly provided jointly by the AC, electric chiller (EC), and T / CES, which helps reduce the capacity configuration requirements of the EC and AC. In addition to power generation, the CCHP also uses the generated thermal energy for cooling through the AC.

[0196] The preferred specific embodiments of the present invention have been described in detail above. It should be understood that those of ordinary skill in the art can make many modifications and variations based on the concept of the present invention without creative labor. Therefore, all technical solutions that can be obtained by those skilled in the art in the technical field based on the concept of the present invention through logical analysis, reasoning, or limited experiments on the basis of the prior art should be within the protection scope determined by the claims.

Claims

1. An optimization method for energy system configuration considering the variable operating conditions of equipment and the degradation of energy storage life, characterized in that Including the following steps: Establish a mathematical model of the energy system, which includes a photovoltaic system, a combined cooling, heating and power (CCHP) system, a gas boiler, an electric chiller, an energy storage device, and a cold and heat storage device. Among them, in the mathematical model of the energy storage device, the cycle aging index of the device is calculated considering the energy storage life attenuation, and in the mathematical models of the CCHP system, the gas boiler, and the electric chiller, the variable operating condition characteristics of the device are considered to calculate the corresponding efficiency; Based on the established mathematical model, use the two-layer optimization method to optimize the resource allocation of the energy system. Among them, the upper layer of the two-layer optimization method optimizes the device capacity design with the goal of minimizing the annualized cost, and the lower layer optimizes the operation with the goal of minimizing the sum of the operation cost, the battery life degradation cost, and the demand electricity price. Among them, the battery life degradation cost is calculated based on the device cycle aging index.

2. The optimization method for energy system configuration considering the off-design characteristics of equipment and the degradation of energy storage life according to claim 1, wherein The mathematical model of the photovoltaic system is: P pv = A pv P pv,s A pv = γA roof Among them, P pv,s is the power generation of the photovoltaic panel per unit area, A pv is the photovoltaic area, A roof is the roof area where photovoltaics can be installed, γ is the ratio of the area where photovoltaics can be installed, P pv is the photovoltaic power generation.

3. An optimization method for energy system configuration considering the variable operating conditions of equipment and the degradation of energy storage life according to claim 1, characterized in that The CCHP system includes a gas turbine, a waste heat boiler, and an absorption chiller, where The mathematical model of the gas turbine is: P GT,e = η GT,e F GT L NG Among them, P GT,e is the power generation power of the gas turbine, η GT,e is the power generation efficiency of the gas turbine, F GT is the gas consumption, L NG is the calorific value of natural gas. Among them, the power generation efficiency of the gas turbine is determined considering the off-design characteristics of the equipment: η GT = a1 × PLR GT 2 + b1 × PLR GT + c1 where a1, b1, and c1 are electrical efficiency coefficients, PLR GT is the gas turbine part load ratio, and CAP GT is the rated capacity of the gas turbine; The mathematical model of the waste heat boiler is: Q WHB,h = η GT,h F GT L NG Among them, Q WHB,h is the heating capacity of the waste heat boiler, and η GT,h is the thermal efficiency of the gas turbine, which is determined considering the off-design characteristics of the equipment: η GT,h = a2 × PLR GT 2 + b2 × PLR GT + c2 Among them, a2, b2, and c2 are the thermal efficiency coefficients of the gas turbine; The mathematical model of the absorption chiller is: L AC,c = Q AC,h η AC Among them, L AC,c is the cooling capacity of the absorption chiller, Q AC,h is the heat input to the absorption chiller, and η AC is the efficiency of the absorption chiller.

4. An optimization method for energy system configuration considering the characteristics of equipment under variable operating conditions and the degradation of energy storage life according to claim 1, characterized in that The mathematical model of the gas boiler is: Q GB = η GB F GB L NG Among them, Q GB is the heating capacity of the gas boiler, F GB is the gas consumption of the gas boiler, L NG is the calorific value of natural gas, η GB is the boiler efficiency, which is determined considering the variable operating conditions of the equipment: η GB = a3 × PLR GB 2 + b3 × PLR GB + c3 Among them, a3, b3, and c3 are the thermal efficiency coefficients of the gas boiler, and CAP GB is the rated capacity of the gas boiler, and PLR GB is the partial load ratio of the gas boiler.

5. An optimization method for energy system configuration considering the variable operating conditions of equipment and the degradation of energy storage life according to claim 1, characterized in that The mathematical model of the electric chiller is: L EC = P EC,in EER EC Among them, L EC is the cooling capacity output by the electric refrigeration unit, and P EC,in is the power consumption of the electric refrigeration unit. EER EC is the energy efficiency ratio of the unit, which is determined considering the variable operating conditions of the equipment: EER EC = a4 × PLR EC 2 + b4 × PLR EC + c4 Among them, a4, b4, and c4 are refrigeration efficiency coefficients, and PLR EC is the part-load ratio of the electric refrigeration unit.

6. The optimization method for energy system configuration considering the variable operating conditions of the device and the degradation of energy storage life according to claim 1, characterized in that The mathematical model of the energy storage device is: where BES(t) is the battery power at time t, and are the battery charge and discharge efficiencies respectively, and are the battery charge and discharge powers respectively, Δt is the time interval, and σ BES is the battery attenuation coefficient; Considering the influence of cycle aging, calculate the device cycle aging index: Among them, EFC provides the total energy for a complete charge-discharge cycle of the energy storage system, CAP BES is the capacity of the electricity storage device, is the number of cycles when the battery health SOH = 80%.

7. An optimization method for energy system configuration considering the characteristics of equipment under variable operating conditions and the degradation of energy storage life, characterized in that, The mathematical model of the cold and heat storage device is: Among them, T / CES(t) is the capacity of the cold storage equipment at time t, and σ T / CES is the self-heat release coefficient, and are the cold storage and heat release efficiencies respectively, and are the cold storage and heat release quantities respectively, and Δt is the time interval.

8. The optimization method for energy system configuration considering the variable operating conditions of equipment and the degradation of energy storage life according to claim 1, characterized in that The optimization objective of the upper layer of the double-layer optimization method is to minimize the annualized cost C total : C total = C fac + C om + C lower Among them, C fac , C om , C lower are the equipment investment cost, operation and maintenance cost, and the operation cost of the lower layer calculation respectively. CRF k is the capacity recovery factor of equipment k, CAP k is the equipment capacity of equipment k, f k,buy is the unit cost of equipment k, f k,om is the unit operation and maintenance cost of equipment k, r is the annual interest rate, l k is the service life of equipment k; The constraint conditions of the upper layer include the rated capacity constraints of each device.

9. An optimization method for energy system configuration considering the characteristics of equipment under variable operating conditions and the degradation of energy storage life, characterized in that, The optimization objective of the lower layer is to minimize C lower : C lower = C op + C bd + C cp Among them, C op , C bd , C cp are the operating cost, the battery life degradation cost, and the demand electricity price respectively. β ele,buy,t is the unit power purchase cost, T is the time, P grid,buy (t) is the grid power purchase volume, β gas is the gas purchase cost, F GT is the gas consumption of the gas turbine, F GB is the gas consumption of the gas boiler, PVF is the net present value, k is the maximum allowable capacity decay percentage at the end of the battery life, AGE cyc (t) is the cycle life, is the battery price in the l k th year, i is the interest rate, l k is the service life of equipment k, υ is the unit demand electricity price, P buy is the power purchase volume, and n is the number of months; The constraint conditions of the lower layer include: energy balance constraint, device operation constraint, and energy storage operation constraint.

10. The optimization method for energy system configuration considering the variable operating conditions of equipment and the degradation of energy storage life according to claim 1, characterized in that The two-layer optimization method is solved using the genetic algorithm in the upper layer and the interior point optimizer in the lower layer, including the following steps: S1, Obtain the basic parameters and conditions, including: genetic algorithm parameters, unit device capacity investment cost, device operation and maintenance cost, and maximum device capacity limit; S2, Encode the upper-layer decision variables using the genetic algorithm; S3, Generate the initial population; S4, Use the interior point optimizer to perform the lower-layer optimization solution to obtain the device hourly output power; S5, Calculate the individual fitness value based on the solution result of the lower layer; S6, Judge whether the convergence condition is satisfied. If so, output the final optimization result. Otherwise, generate a new population and perform selection, crossover, and mutation operations based on the individual fitness value, and return to step S4 for optimization solution.

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