An optimal configuration method of source-network-load-storage integrated microgrid
By establishing a time-varying performance degradation model and a hierarchical optimization strategy, the problem of uncoordinated performance degradation of electrical, cooling, and thermal energy storage equipment in microgrids was solved, enabling economic assessment and equipment life extension throughout the entire life cycle, and improving the operating efficiency and economy of microgrids.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SOUTHEAST UNIV
- Filing Date
- 2026-03-19
- Publication Date
- 2026-07-31
AI Technical Summary
In existing technologies, microgrid optimization configuration methods fail to uniformly consider the performance degradation of electric, cold, and thermal energy storage devices, resulting in inaccurate full life cycle economic assessments and configuration results that fail to achieve the expected economic benefits during dynamic operation.
A time-varying performance degradation model is established, including a capacity degradation model for electrochemical energy storage devices, a heat loss coefficient model for thermal storage devices, and a heat exchange efficiency model for cold storage devices. An optimal configuration model is constructed with the goal of minimizing the net present cost over the entire life cycle. The model is then used to perform hierarchical optimization by combining load and renewable energy output to generate an optimal operation control strategy.
It enables more accurate economic assessment throughout the entire life cycle, extends equipment lifespan, improves the overall return on investment and operating efficiency of microgrids, and enhances the economic efficiency of integrated energy utilization through multi-energy complementary arbitrage.
Smart Images
Figure CN122495546A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of microgrid planning and operation technology, and in particular to an optimized configuration method for an integrated source-grid-load-storage microgrid. Background Technology
[0002] With the increasing penetration of renewable energy and the diversification of energy consumption patterns, integrated microgrids that combine distributed power sources, energy storage systems, and cooling, heating, and power loads ("generation, grid, load, and storage") are becoming a development trend. Optimized configuration is key to ensuring the economical and reliable operation of microgrids.
[0003] In existing technologies, microgrid optimization configuration methods mostly focus on the capacity configuration and scheduling of electrochemical energy storage (such as lithium batteries), and their lifetime models are usually based on throughput or depth of discharge. However, for generalized integrated energy systems that include both cold storage (such as water-based and ice-based cold storage) and thermal storage (such as water-based and phase change thermal storage), existing research has significant shortcomings: Fragmented modeling: Most studies consider electrical energy storage separately from cold / thermal energy storage, or treat the latter as static devices only, failing to model them within a unified optimization framework.
[0004] Lifetime model missing: For cold and hot energy storage units, existing methods generally ignore their performance degradation mechanisms (such as the heat loss rate of hot water storage tanks increases with the aging of the insulation layer and the heat exchange efficiency of cold storage devices decreases), or use overly simplified fixed loss coefficients, resulting in distorted long-term operating economic assessments.
[0005] Control and configuration disconnect: The simplified model used in the configuration phase is incompatible with the complex multi-timescale control strategies in actual operation, which makes the configuration results unable to achieve the expected economic efficiency in dynamic operation.
[0006] Therefore, there is an urgent need for a microgrid planning method that can uniformly quantify the performance degradation and cost impact of all types of energy storage devices (electric, cooling, and heating) during long-term operation, and based on this, conduct coordinated configuration and optimized operation. Summary of the Invention
[0007] To address the shortcomings of existing technologies, this invention provides an optimized configuration method for an integrated microgrid of source, grid, load, and storage, which solves the problem that existing technologies fail to uniformly consider the performance degradation of electrical, cooling, and thermal energy storage, leading to inaccurate life-cycle economic assessments.
[0008] The technical solution adopted in this invention is as follows: This invention provides an optimized configuration method for an integrated microgrid (source-grid-load-storage), wherein the microgrid includes an energy supply side, a network side, electrical loads, cooling loads, heating loads, an electrochemical energy storage device, a thermal storage device, and a cold storage device. The method includes: A time-varying performance degradation model is established, which includes: establishing a capacity degradation model for the electrochemical energy storage device, which describes the degradation of battery capacity with the equivalent number of cycles and the depth of discharge; modeling the heat loss coefficient of the thermal storage device as a variable that increases exponentially with the service life to obtain a thermal storage performance degradation model; and modeling the heat exchange efficiency of the cold storage device as a variable that decreases linearly or nonlinearly with the increase of the number of operating cycles to obtain a cold storage performance degradation model. With the goal of minimizing the net present cost of the entire life cycle of the microgrid, an optimization configuration model is constructed. Its objective function takes into account the equipment investment cost, the operation and maintenance cost related to performance degradation, and the equipment replacement cost triggered by the time-varying performance degradation model. The constraints include the energy storage equipment operation constraints defined by the time-varying performance degradation model. Using load, renewable energy output, and regional peak-valley electricity price signals as inputs, the optimization configuration model is solved hierarchically, and an optimized operation control strategy is generated.
[0009] The preferred technical solution is: The triggering conditions for the equipment replacement cost include: When the usable capacity of the electrochemical energy storage device decays to the first threshold of the rated capacity, or when the heat loss coefficient of the thermal storage device rises to the second threshold, or when the heat exchange efficiency of the cold storage device drops to the third threshold, the replacement cost of the corresponding equipment shall be taken into account.
[0010] The thermal storage performance degradation model includes a time-varying heat loss coefficient model. ,in For the device in use Heat loss coefficient after the year Here, α is the initial heat loss coefficient, α is the material aging coefficient, and the thermal storage dynamic model is:
[0011] in, for t The heat storage capacity of the time-sensing device is subject to certain constraints, where A is the heat dissipation area of the device, and ΔT is the temperature difference between the heat dissipation surface and the environment. The rated capacity of the thermal storage device, The specific heat capacity of the working fluid, For heat storage power and heat release power, , For the heat storage efficiency and heat release efficiency of the thermal storage device, For time step.
[0012] The cold storage performance degradation model includes a time-varying thermal efficiency model. ,in For the device to complete the cumulative total Heat exchange efficiency after one cycle The initial heat exchange efficiency, Material aging coefficient; and cold storage dynamic model:
[0013] in, for t The cold storage capacity of the time-limiting device is subject to certain constraints. For the rated capacity of the device, The specific heat capacity of the working fluid, For cold storage power and cold release power, , These are the base efficiencies for the charging and discharging cooling processes, respectively. For time-varying thermal efficiency, For time step.
[0014] The electrochemical energy storage capacity decay model includes the available capacity model. ,in, For the rated capacity of the device, This is the capacity decay coefficient. , They are respectively t The available capacity and cumulative equivalent depth of discharge at each moment, and:
[0015] in, For charge-discharge cycle energy, This is a time index variable, with values ranging from 1 to t. The number of cycles is the rated number. And energy storage model:
[0016] in, for t Stores electricity at all times. , For charging and discharging power, , For charging and discharging efficiency, For time step; Subject to operational constraints: ,in, , These represent the minimum and maximum values of the charged state.
[0017] The objective function of the optimized configuration model includes:
[0018] In the above formula: Represents the equipment operation period Year, Total number of years of operation The discount rate; Investment costs: , This refers to the entire pool of potential equipment, including power supply equipment, conversion equipment, and energy storage equipment. , respectively equipment Rated power and rated capacity, , respectively equipment Unit power cost and unit capacity cost; Annual operation and maintenance costs: ,in, , , They are respectively Electricity price at any given time, grid interconnection power, and gas consumption. For gas prices, Maintenance factor related to equipment condition. A collection of energy storage devices, For the first y Annual equipment j The performance degradation function is calculated from the time-varying performance degradation model. Energy storage equipment replacement cost: When the equipment reaches the end of its lifespan, the indicator variable... Or 0, This indicates that device j reaches the end of its lifespan in year y and needs to be replaced. 0 indicates that device j does not need to be replaced in year y.
[0019] The constraints of the optimization configuration model include power balance constraints, equipment operating characteristic constraints, grid interaction constraints, and energy storage equipment operating constraints. The limits for the operation constraints of the energy storage device include the limits for heat storage, cold storage, charge-discharge cycle capacity, and available capacity, determined according to the time-varying performance degradation model.
[0020] The step of performing hierarchical optimization solution on the optimized configuration model and generating an optimized operation control strategy includes: Long-term configuration optimization involves solving the optimized configuration model using typical year data to obtain equipment capacity and power. The short-term optimization scheduling layer aims to minimize daily operating costs and generates the optimal scheduling plan for the next 24 hours based on the current actual equipment degradation status and ultra-short-term forecast data.
[0021] In the short-term optimized scheduling layer, the peak-valley electricity price difference in the region is used to coordinate and control the electrochemical energy storage to charge during the off-peak period and discharge during the peak period. At the same time, the cold and heat storage devices are controlled to store energy during the off-peak period or when the cold and heat loads are low, and to release energy during the peak period or when the load is high, so as to achieve comprehensive arbitrage.
[0022] The optimal configuration model is solved using a genetic algorithm, particle swarm optimization algorithm, or other heuristic algorithms.
[0023] The technical solution of the present invention can achieve at least some of the following beneficial effects: This invention creatively considers the time-varying performance degradation of electrochemical energy storage, cold storage, and thermal storage devices simultaneously in microgrid optimization configuration. It achieves unified and refined modeling that considers the lifespan impact of all types of energy storage across the entire chain, resulting in a more realistic lifecycle cost model. This makes the configuration results more economical in the long term, improving the overall return on investment and operational efficiency of the microgrid. Through refined model construction, it can respond to the performance degradation of energy storage devices over time, ensuring that the planning scheme remains in an optimal or near-optimal state throughout its lifecycle, extending the service life of key equipment, reducing overall operational risks, and thus improving the robustness and adaptability of microgrid planning. Furthermore, by coupling the cold, heat, and electrical energy storage models with regional peak-valley electricity prices, it can intelligently decide whether to store electricity, cold, or heat during off-peak hours, or prioritize the release of energy during peak hours, maximizing arbitrage under multi-energy complementarity, thereby improving the economic efficiency of integrated energy utilization.
[0024] The hierarchical optimization strategy of this invention achieves an organic integration of configuration and operational control. Through the hierarchical optimization framework of "long-term configuration - short-term scheduling - real-time control", the feasibility of the configuration scheme in dynamic operation is ensured, and the generated scheduling and control strategies directly guide the system operation.
[0025] The method of this invention is not limited by the type, scale and geographical location of microgrids. It can be applied to any microgrid, active distribution network and integrated energy system involving energy storage configuration, and has strong versatility.
[0026] Other features and advantages of the invention will be set forth in the following description or may be learned by practicing the invention. Attached Figure Description
[0027] Figure 1 This is a flowchart of a method according to an embodiment of the present invention.
[0028] Figure 2 This is a diagram illustrating the hierarchical optimization and control strategy framework of an embodiment of the present invention.
[0029] Figure 3 This is a time-of-use electricity price diagram according to an embodiment of the present invention.
[0030] Figure 4 This is a configuration scheme for the capacity of key equipment in the embodiments and comparative schemes of the present invention.
[0031] Figure 5 The present invention provides an embodiment and comparative scheme for energy storage capacity configuration.
[0032] Figure 6 The breakdown of costs for the embodiments and comparative schemes of this invention.
[0033] Figure 7 This is a comparison of the full lifecycle costs of the embodiments of the present invention and the comparative schemes. Detailed Implementation
[0034] The specific embodiments of the present invention are described below with reference to the accompanying drawings.
[0035] This embodiment presents an optimized configuration method for an integrated microgrid with source, grid, load, and storage. The microgrid is a combined cooling, heating, and power (CCHP) microgrid, comprising an energy supply side, a network side, an electrical load (Pload), a cooling load side (Qcload), a heating load side (Qhload), an electrochemical energy storage device (BESS), a thermal energy storage device (HTES), and a cold energy storage device (CTES).
[0036] Preferably, the functional side includes, but is not limited to, photovoltaic (PV), wind turbine (WT), gas turbine (MT), electric chiller (EC), absorption chiller (AC), and gas boiler (GB). The network side includes AC bus, DC bus, heating network, and cooling network. The microgrid interacts with the main grid through power exchange (Pgrid), and the electricity price adopts a time-of-use pricing model.
[0037] See Figure 1 The method includes: S1. Establish a time-varying performance degradation model, which includes: A capacity decay model is established for the electrochemical energy storage device to describe the decay of battery capacity with the equivalent number of cycles and the depth of discharge; the heat loss coefficient of the thermal storage device is modeled as a variable that increases exponentially with the service life to obtain a thermal storage performance decay model; the heat exchange efficiency of the cold storage device is modeled as a variable that decreases linearly or nonlinearly with the increase of the number of operating cycles to obtain a cold storage performance decay model.
[0038] As a preferred embodiment, the capacity decay model includes an available capacity model. ,in, For the rated capacity of the device, This is the capacity decay coefficient. , They are respectively t The available capacity and cumulative equivalent depth of discharge at each moment, and:
[0039] in, For charge-discharge cycle energy, For time index variables, The number of cycles is the rated number. And energy storage model:
[0040] in, for t Stores electricity at all times. , For charging and discharging power, , For charging and discharging efficiency, For time step; Subject to operational constraints: ,in, , These represent the minimum and maximum values of the charged state.
[0041] As can be seen, the capacity decay model described in this embodiment is a lifetime decay model based on the equivalent number of cycles and the depth of discharge, which effectively describes the changes in internal resistance and capacity decay of the device over time.
[0042] As a preferred embodiment, the thermal storage device includes a water-based thermal storage tank, and its key degradation lies in the increased heat loss rate caused by a decline in insulation performance. The thermal storage performance degradation model preferably includes a time-varying heat loss coefficient model. ,in For the device in use Heat loss coefficient after the year, kW / ℃ Here, α is the initial heat loss coefficient, α is the material aging coefficient (>0); and the thermal storage dynamic model is as follows:
[0043] in, for t The heat storage capacity of the timekeeping device is subject to limit constraints. A represents the heat dissipation area of the device, and ΔT represents the temperature difference between the heat storage tank and the environment. For the rated capacity of the device, The specific heat capacity of the working fluid, For heat storage power and heat release power, , For the heat storage efficiency and heat release efficiency of the thermal storage device, For time step.
[0044] As a preferred embodiment, the cold storage device includes phase change cold storage devices such as ice storage, and its degradation is mainly reflected in the decrease in phase change heat transfer efficiency. The cold storage performance degradation model preferably includes a time-varying heat transfer efficiency model. ,in For the device to complete the cumulative total Heat exchange efficiency after one cycle The initial heat exchange efficiency, The material aging coefficient, Specifically, this includes the number of freezing-melting cycles and the dynamic model of cold storage:
[0045] in, for t The cold storage capacity of the time-limiting device is subject to limit constraints. , For the rated capacity of the device, The specific heat capacity of the working fluid, For cold storage power and cold release power, , These are the basic efficiencies of the cooling and cooling processes, respectively (i.e., fixed losses considering pump energy consumption, heat exchange temperature difference, etc.). For time-varying thermal efficiency, For time step.
[0046] Thus, this embodiment constructs a refined charge-discharge model that reflects the dynamic changes in energy storage state and includes attenuation characteristics.
[0047] S2. To minimize the net present cost of the microgrid throughout its entire lifecycle, an optimization configuration model is constructed. Its objective function takes into account equipment investment costs, operation and maintenance costs related to performance degradation, and equipment replacement costs triggered by the time-varying performance degradation model. The constraints include the energy storage equipment operation constraints defined by the time-varying performance degradation model.
[0048] As a preferred embodiment, the triggering conditions for the equipment replacement cost include: When the usable capacity of an electrochemical energy storage device decays to a first threshold of its rated capacity, or when the heat loss coefficient of a thermal storage device rises to a second threshold, or when the heat exchange efficiency of a cold storage device decreases to a third threshold, the replacement cost of the corresponding equipment is taken into account. Each threshold is determined by a time-varying performance degradation model corresponding to electricity, cold, and heat.
[0049] Specifically, the objective function of the optimized configuration model includes:
[0050] In the above formula: Represents the equipment operation period Year, Total number of years of operation The discount rate is set at 5% to 8%. Investment costs: , This refers to the entire pool of potential equipment, including power supply equipment, conversion equipment, and energy storage equipment. , respectively equipment Rated power and rated capacity, , respectively equipment Unit power cost and unit capacity cost; Annual operation and maintenance costs: ,in, , , They are respectively Electricity price at any given time, grid interconnection power, and gas consumption. For gas prices, Maintenance factor related to equipment condition. A collection of energy storage devices, For the first y Annual equipment j The performance degradation function is calculated from the time-varying performance degradation model. Equipment replacement cost When the equipment reaches the end of its lifespan, the indicator variable... Or 0, This indicates that device j reaches the end of its lifespan in year y and needs to be replaced. 0 indicates that device j does not need to be replaced in year y.
[0051] The constraints of the optimized configuration model include: Power balance constraints include three types of constraints: electrical, cooling, and thermal. Electric power balance constraints: ,in, For grid interaction power, For the power generation of gas turbines, Photovoltaic power generation capacity, This refers to the battery discharge power. For electrical load, The power consumption of the electric chiller Battery charging power; Cold power balance constraints: ,in, For absorption refrigeration, the refrigeration power is... For the refrigeration power of the electric refrigeration unit, This refers to the cooling capacity of the cold storage device. Thermal power balance constraint: ,in, For boiler heating capacity, This refers to the heat release power of the thermal storage device. For heat load, This refers to the heat release power of the absorption refrigerant. The power to charge the thermal storage device.
[0052] The constraints include equipment operating characteristics, grid interaction constraints, and energy storage equipment operating constraints. The limits of the energy storage equipment operating constraints include the limits of heat storage, cold storage, charge-discharge cycle capacity, and available capacity determined according to the time-varying performance decay model.
[0053] S3. Using load, renewable energy output, and regional peak-valley electricity price signals as inputs, perform hierarchical optimization solutions on the optimized configuration model to obtain scheduling plans, configuration schemes, and generate optimized operation control strategies.
[0054] As a preferred method, see Figure 2 The step of performing hierarchical optimization solution on the optimized configuration model to obtain the scheduling plan, configuration scheme, and generate optimized operation control strategy includes: S31. Long-term configuration optimization: Solve the optimization configuration model using typical annual data to obtain equipment capacity and power.
[0055] Specifically, in the long-term configuration process, the optimization period is defined as one year (T=8760 hours), with a time resolution of 1 hour. Load, renewable energy output, and electricity price data for a typical year (or a multi-year series) are used as inputs. All load, renewable energy output, and electricity price data are presented as known inputs in time series form. Heuristic algorithms such as genetic algorithms and particle swarm optimization are used to solve the optimization configuration model. The output of the solution is the optimal rated power and capacity for various types of equipment. This process incorporates simulations of the time-varying degradation process of energy storage devices, ensuring that the economic efficiency of the configuration scheme is optimal throughout its entire lifecycle.
[0056] S32. Short-term optimization scheduling layer: With the goal of minimizing daily operating costs, it generates the optimal scheduling plan for the next 24 hours based on the current actual equipment degradation status and ultra-short-term forecast data.
[0057] Specifically, under the fixed equipment configuration from step S31, the short-term optimization scheduling layer establishes a 24-hour rolling optimization scheduling model with the goal of minimizing daily operating costs. Furthermore, the performance parameters used in the energy storage constraints of the rolling optimization scheduling model are actual values at the current moment, rather than fixed values. The model uses ultra-short-term forecast data for the next 24 hours, along with the current health status of the energy storage equipment (SOC and the performance degradation parameters involved in the time-varying performance degradation model), as input. It is solved every 24 hours to generate a detailed scheduling instruction set for each moment of the next day, guiding the operation of each device.
[0058] The short-term optimization scheduling layer is responsible for smoothing out fluctuations in renewable energy and load, ensuring system stability, and aligning as closely as possible with the economic objectives of the current scheduling plan. Preferably, the short-term optimization scheduling layer utilizes regional peak-valley electricity price differences to coordinate and control electrochemical energy storage to charge during off-peak hours and discharge during peak hours. Simultaneously, it controls cold and heat storage devices to store energy during off-peak hours or periods of low cold and heat loads, and release energy during peak hours or periods of high loads, thereby achieving comprehensive arbitrage.
[0059] The effectiveness of the method in this embodiment will be further verified by a specific example below.
[0060] To verify the effectiveness of the method proposed in this invention, this example uses a microgrid in an industrial park as the object, and compares the optimization configuration and operation simulations using three schemes: Scheme A (the scheme of this invention): a time-varying performance degradation model that fully considers the energy storage devices (electric, cold, and heat storage); Scheme B (no degradation): all energy storage devices are considered to have no degradation (fixed efficiency, fixed heat loss, infinite lifespan); Scheme C (only considering energy degradation): only electrochemical energy storage considers capacity degradation, and cold and heat storage are considered to have no degradation. By comparing the net present cost (NPC) and configuration results of the three schemes over their entire life cycle, the significant impact of cold and heat storage degradation on the economics of microgrid planning is revealed, thereby verifying the effectiveness of this invention.
[0061] (1) System Architecture The microgrid example includes: photovoltaic (PV), wind power (WT), gas turbine (MT), electric chiller (EC), absorption chiller (AC), gas boiler (GB), electrochemical energy storage (BESS), thermal energy storage tank (HTES), and ice storage system (CTES). The system is connected to the main grid and implements time-of-use pricing. Time-of-use pricing is as follows: Figure 3 As shown.
[0062] The economic parameters of the equipment are shown in Table 1: Table 1 Equipment Technical and Economic Parameters
[0063] (2) Capacity optimization configuration results: The three schemes were optimized throughout their entire lifecycles (using Gurobi as the solver, and the model was transformed into MILP through linearization) to obtain the optimal installed capacity of key equipment, as follows: Figure 4 As shown, the optimal energy storage capacity configuration scheme is as follows: Figure 5 As shown.
[0064] The configuration results show that: Scheme B (no degradation) optimistically estimates the long-term performance of thermal energy storage, thus configuring a larger thermal storage tank and ice storage capacity, while also having a relatively large electrical energy storage capacity, attempting to replace gas consumption by storing large amounts of cold / heat during periods of low prices. Scheme C (electrical degradation only), although considering battery aging, still overestimates the effectiveness of thermal energy storage, resulting in thermal and cold storage capacities falling between Scheme A and Scheme B. Scheme A (this invention), anticipating the performance degradation of thermal energy storage in the later stages (increased heat loss, reduced heat exchange efficiency), appropriately reduces the thermal storage tank and ice storage capacity, instead configuring a slightly larger gas turbine and electric chiller as a supplement, while also having a more reasonable electrical energy storage capacity, avoiding accelerated aging due to frequent deep charge-discharge cycles.
[0065] (3) Comparison of life cycle costs With the configuration results of the three schemes fixed, a 20-year full lifecycle operation simulation was conducted using the same operational optimization strategy (day-ahead scheduling + real-time MPC control). Schemes A and C update the energy storage model parameters based on the current health status during scheduling, while scheme B always uses the initial parameters. The costs for each year are accumulated and discounted to obtain the itemized costs as follows: Figure 6 As shown, the total lifecycle net present value (NPC) is as follows: Figure 7 As shown.
[0066] Through specific numerical examples, the optimized configuration method proposed in this invention, which considers the time-varying degradation of all types of energy storage, can reduce the total cost by more than 5% over the entire life cycle compared to traditional methods that do not consider degradation or only consider the degradation of electrical energy storage. This benefit stems from a more accurate initial configuration and a more intelligent degradation-adaptive operation strategy, fully validating the effectiveness and engineering practical value of this invention.
[0067] In summary, this invention incorporates the performance degradation mechanism of cold and thermal energy storage units and the electrochemical energy storage lifetime model into a unified, time-varying full life cycle cost analysis framework, and couples it with regional peak-valley electricity price signals to achieve coordinated optimization configuration and scheduling of cold storage, thermal storage, and electricity storage. This constructs a unified time-varying performance degradation model encompassing the cyclic degradation of electrochemical energy storage, the thermal insulation performance degradation of thermal storage devices, and the decline in heat exchange efficiency of cold storage devices. An optimization configuration model is established with the goal of minimizing the net present cost over the entire life cycle, and the aforementioned time-varying performance degradation model is embedded with constraints to accurately reflect the evolution of energy storage status. Furthermore, a hierarchical optimization architecture is adopted: the long-term planning layer solves for the optimal equipment configuration; the short-term scheduling layer performs day-ahead economic scheduling based on the real-time degradation status of equipment and peak-valley electricity prices. This invention provides unified modeling and coordinated optimization of the "full-link, full-type" life cycle factors of energy storage systems, achieving accurate assessment of the system's long-term economic performance and significantly improving the overall economic efficiency of microgrid planning and operation.
[0068] It will be understood by those skilled in the art that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.
Claims
1. A method for optimizing the configuration of an integrated microgrid (source-grid-load-storage), wherein the microgrid comprises an energy supply side, a network side, electrical loads, cooling loads, heating loads, an electrochemical energy storage device, a thermal storage device, and a cold storage device, characterized in that... The method includes: A time-varying performance degradation model is established, which includes: establishing a capacity degradation model for the electrochemical energy storage device, which describes the degradation of battery capacity with the equivalent number of cycles and the depth of discharge; modeling the heat loss coefficient of the thermal storage device as a variable that increases exponentially with the service life to obtain a thermal storage performance degradation model; and modeling the heat exchange efficiency of the cold storage device as a variable that decreases linearly or nonlinearly with the increase of the number of operating cycles to obtain a cold storage performance degradation model. With the goal of minimizing the net present cost of the entire life cycle of the microgrid, an optimization configuration model is constructed. Its objective function takes into account the equipment investment cost, the operation and maintenance cost related to performance degradation, and the equipment replacement cost triggered by the time-varying performance degradation model. The constraints include the energy storage equipment operation constraints defined by the time-varying performance degradation model. Using load, renewable energy output, and regional peak-valley electricity price signals as inputs, the optimization configuration model is solved hierarchically, and an optimized operation control strategy is generated.
2. The method according to claim 1, characterized in that, The triggering conditions for the equipment replacement cost include: When the usable capacity of the electrochemical energy storage device decays to the first threshold of the rated capacity, or when the heat loss coefficient of the thermal storage device rises to the second threshold, or when the heat exchange efficiency of the cold storage device drops to the third threshold, the replacement cost of the corresponding equipment shall be taken into account.
3. The method according to claim 1, characterized in that, The thermal storage performance degradation model includes a time-varying heat loss coefficient model. ,in For the device in use Heat loss coefficient after the year α is the initial heat loss coefficient, and α is the material aging coefficient; And thermal storage dynamic model: ， in, for t The heat storage capacity of the time-sensing device is subject to certain constraints, where A is the heat dissipation area of the device, and ΔT is the temperature difference between the heat dissipation surface and the environment. The rated capacity of the thermal storage device, The specific heat capacity of the working fluid, For heat storage power and heat release power, , For the heat storage efficiency and heat release efficiency of the thermal storage device, For time step.
4. The method according to claim 1, characterized in that, The cold storage performance degradation model includes a time-varying thermal efficiency model. ,in For the device to complete the cumulative total Heat exchange efficiency after one cycle The initial heat exchange efficiency, Material aging coefficient; and cold storage dynamic model: ， in, for t The cold storage capacity of the time-limiting device is subject to certain constraints. For the rated capacity of the device, The specific heat capacity of the working fluid, For cold storage power and cold release power, , These are the base efficiencies for the charging and discharging processes, respectively. For time-varying thermal efficiency, For time step.
5. The method according to claim 1, characterized in that, The electrochemical energy storage capacity decay model includes the available capacity model. ,in, For the rated capacity of the device, This is the capacity decay coefficient. , They are respectively t The available capacity and cumulative equivalent depth of discharge at each moment, and: ， in, For charge-discharge cycle energy, This is a time index variable, with values ranging from 1 to t. The number of cycles is the rated number. And energy storage model: ， in, for t Stores electricity at all times. , For charging and discharging power, , For charging and discharging efficiency, For time step; Subject to operational constraints: ,in, , These represent the minimum and maximum values of the charged state.
6. The method according to claim 1, characterized in that, The objective function of the optimized configuration model includes: ， In the above formula: Represents the equipment operation period Year, Total number of years of operation The discount rate; Investment costs: , This refers to the entire pool of potential equipment, including power supply equipment, conversion equipment, and energy storage equipment. , respectively equipment Rated power and rated capacity, , respectively equipment Unit power cost and unit capacity cost; Annual operation and maintenance costs: ,in, , , They are respectively Electricity price at any given time, grid interconnection power, and gas consumption. For gas prices, Maintenance factor related to equipment condition. A collection of energy storage devices, For the first y Annual equipment j The performance degradation function is calculated from the time-varying performance degradation model. Energy storage device replacement cost: When the equipment reaches the end of its lifespan, the indicator variable... Or 0, This indicates that device j reaches the end of its lifespan in year y and needs to be replaced. 0 indicates that device j does not need to be replaced in year y.
7. The method according to claim 1, characterized in that, The constraints of the optimization configuration model include power balance constraints, equipment operating characteristic constraints, grid interaction constraints, and energy storage equipment operating constraints. The limits for the operation constraints of the energy storage device include the limits for heat storage, cold storage, charge-discharge cycle capacity, and available capacity, determined according to the time-varying performance degradation model.
8. The method according to claim 1, characterized in that, The step of performing hierarchical optimization solution on the optimized configuration model and generating an optimized operation control strategy includes: Long-term configuration optimization involves solving the optimized configuration model using typical year data to obtain equipment capacity and power. The short-term optimization scheduling layer aims to minimize daily operating costs and generates the optimal scheduling plan for the next 24 hours based on the current actual equipment degradation status and ultra-short-term forecast data.
9. The method according to claim 8, characterized in that, In the short-term optimized scheduling layer, the peak-valley electricity price difference in the region is used to coordinate and control the electrochemical energy storage to charge during the off-peak period and discharge during the peak period. At the same time, the cold and heat storage devices are controlled to store energy during the off-peak period or when the cold and heat loads are low, and to release energy during the peak period or when the load is high, so as to achieve comprehensive arbitrage.
10. The method according to claim 8, characterized in that, The optimal configuration model is solved using a genetic algorithm, particle swarm optimization algorithm, or other heuristic algorithms.