A method for optimizing configuration of optical storage system based on mixed integer programming

Abstract

The application discloses a kind of based on mixed integer programming's light storage system optimization configuration method, it is related to light storage system optimization technical field, the present application includes the time series data and equipment parameters of project related as model input are comprehensively collected;Establish the mixed integer programming model with the shortest investment payback period as target, including equipment selection and operation strategy variable;Calling mathematical optimization solver calculates model, obtains optimal configuration and operation scheme;The solution result is parsed as specific equipment configuration list, operation strategy instruction and economic analysis report.The present application is globally optimized to photovoltaic capacity, energy storage model and quantity, inverter selection and hour-by-hour charging and discharging strategy by constructing and solving mixed integer programming model, can scientifically quantify the cost and benefit in the whole life cycle of system, automatically generate the configuration scheme and executable operation strategy table more optimal in technology and economy.

Description

Technical Field

[0001] This invention belongs to the field of photovoltaic-storage system optimization technology, and in particular relates to a method for optimizing the configuration of photovoltaic-storage systems based on mixed integer programming. Background Technology

[0002] Currently, the design of commercial and industrial self-use photovoltaic-energy storage systems generally relies on experience-based configuration methods. Engineers estimate the photovoltaic capacity based on roof area and local sunlight conditions; the energy storage capacity is simply determined as a percentage (e.g., 10%-30% of the photovoltaic capacity); and equipment such as inverters are selected based on peak power. After system completion, the energy management system (EMS) control strategy is mostly a fixed pattern, such as "two charging and two discharging": charging during off-peak hours and midday when photovoltaic output is excessive, and discharging during morning and evening peak hours. While this method achieves economic practicality within the constraints of human experience, it still has many shortcomings: The fine-grained temporal coupling relationship among photovoltaic output, load, and electricity price has been neglected. Photovoltaic output, enterprise electricity load, and time-of-use electricity price all exhibit significant temporal fluctuations and periodicities. Existing methods mostly use daily averages or typical daily peak values ​​for static matching, failing to simulate the dynamic energy flow of the system on a continuous time scale in a refined manner, and in particular, failing to fully consider the interaction between energy storage charging and discharging strategies and the aforementioned time-series curves.

[0003] The profound two-way impact of configuration schemes and operational strategies has not been adequately considered. There is a strong coupling relationship between capacity configuration and daily operational strategies: larger energy storage capacity can improve arbitrage flexibility but increases investment costs; traditional empirical design cannot achieve a globally optimal solution, easily leading to low equipment utilization or a prolonged investment payback period; empirical selection lacks rigorous scientific reasoning and calculation, leaving significant room for economic optimization. Therefore, the following solutions are proposed to address these issues.

[0004] For the reasons mentioned above, although design based on human experience can be made as reasonable as possible to a certain extent, it is impossible to achieve optimal economic efficiency throughout the entire life cycle, and the investment recovery period is extended. Summary of the Invention

[0005] The purpose of this invention is to provide a method for optimizing the configuration of a photovoltaic-storage system based on mixed integer programming. By constructing and solving a mixed integer programming model, the method performs global collaborative optimization of photovoltaic capacity, energy storage type and quantity, inverter selection, and time-period charging and discharging strategies. This method can scientifically quantify the costs and benefits throughout the entire life cycle of the system and automatically generate a more technically and economically superior configuration scheme and an executable operation strategy table. This solves the problems of suboptimal system configuration and failure to fully realize economic benefits caused by relying on manual experience for static estimation and ignoring the time coupling and the two-way influence of configuration and operation.

[0006] To solve the above-mentioned technical problems, the present invention is achieved through the following technical solution: This invention provides a method for optimizing the configuration of a photovoltaic-storage system based on mixed-integer programming, comprising the following steps: Step S1, Data Preparation: Collect photovoltaic output time-series data, user load time-series data, grid time-of-use electricity price time-series data, as well as equipment parameters and technical and economic parameters of photovoltaic modules, energy storage systems, and inverters at the project site; Step S2, Model Construction: Based on the data and parameters collected in Step S1, a mixed integer programming model is constructed with the goal of minimizing the static investment payback period of the system. This model includes discrete decision variables for equipment selection and capacity configuration, continuous decision variables for determining the system's time-period operating status, and constraints describing the physical operating laws of the system and the logic of equipment selection. Step S3, Model Solving: Input the mixed integer programming model constructed in step S2 into the mathematical optimization solver for solving to obtain the equipment configuration scheme that optimizes the objective function and the corresponding time-period operation strategy within a typical cycle; Step S4, Solution Output: Based on the optimal decision variable values ​​obtained in Step S3, output an equipment configuration list including recommended photovoltaic installed capacity, energy storage system model and configuration quantity, inverter model and configuration quantity, as well as a typical cycle time-period operation strategy.

[0007] Furthermore, in step S2, the objective function of the mixed integer programming model is to minimize the ratio of the total system investment to the expected annual net income after the system is put into operation, wherein the total investment includes the investment costs of photovoltaic, energy storage and inverter, and the annual net income is the difference in annual electricity expenditure before and after the system is put into operation.

[0008] Furthermore, the time-series data collected in step S1 includes: a unit photovoltaic installed capacity output curve characterizing solar resource conditions, a load power curve characterizing user electricity consumption habits, and a grid electricity purchase price curve including peak-valley electricity price differences.

[0009] Furthermore, the discrete decision variables in step S2 include: a binary variable for selecting a specific model of energy storage equipment, an integer variable for determining the number of energy storage equipment configurations, a binary variable for selecting a specific model of inverter, and an integer variable for determining the number of inverter configurations; the continuous decision variables include: photovoltaic installed capacity, and the grid power purchase, energy storage charging and discharging power, energy storage power status, and photovoltaic curtailment power for each time period.

[0010] Furthermore, the constraints described in step S2 include at least: system power generation and consumption balance constraints for each time period, upper limit constraints for photovoltaic installed capacity, constraints on the maximum output of photovoltaics by the total rated power of the inverter, constraints on the dynamic update of energy storage power status, constraints on the safe range of energy storage power, constraints on the upper and lower limits of energy storage charging and discharging power, and constraints on the mutual exclusion of energy storage charging and discharging behavior at the same time.

[0011] Furthermore, the optimal operating strategy obtained in step S3 provides specific power instructions for grid power purchase, energy storage charging, and energy storage discharging for each time interval within a typical cycle, and ensures that the energy storage power status is consistent at the start and end of the cycle.

[0012] Furthermore, the output of step S4 also includes the total system investment, expected annual electricity savings, and core economic indicators such as static investment payback period, calculated based on the optimal configuration and operation strategy.

[0013] The present invention has the following beneficial effects: This invention constructs a hybrid integer programming model to achieve coordinated optimization of the configuration selection of photovoltaic, energy storage, and inverters and their full-cycle operation strategies. It transforms system design from relying on human experience to a scientific decision-making process based on data and algorithms. This method can simultaneously solve for equipment combination and charge / discharge scheduling within a mathematical framework, quantifying the trade-off between investment costs and operating benefits, thereby obtaining a more economical system configuration scheme and helping to optimize investment payback time. At the same time, its refined operation instruction table can directly guide the strategy formulation of the energy management system, improving the economy and rationality of the overall system operation from the source, and playing a positive role in promoting the scientific design and application of user-side photovoltaic and energy storage systems.

[0014] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart illustrating an optimized configuration method for a photovoltaic-storage system based on hybrid integer programming, according to the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Please see Figure 1 As shown, this invention is a method for optimizing the configuration of a photovoltaic-storage system based on mixed-integer programming, comprising the following steps: Step S1, Data Preparation: Collect photovoltaic output time-series data, user load time-series data, grid time-of-use electricity price time-series data, as well as equipment parameters and technical and economic parameters of photovoltaic modules, energy storage systems, and inverters at the project site; Step S2, Model Construction: Based on the data and parameters collected in Step S1, a mixed integer programming model is constructed with the goal of minimizing the static investment payback period of the system. This model includes discrete decision variables for equipment selection and capacity configuration, continuous decision variables for determining the system's time-period operating status, and constraints describing the physical operating laws of the system and the logic of equipment selection. Step S3, Model Solving: Input the mixed integer programming model constructed in step S2 into the mathematical optimization solver for solving to obtain the equipment configuration scheme that optimizes the objective function and the corresponding time-period operation strategy within a typical cycle; Step S4, Solution Output: Based on the optimal decision variable values ​​obtained in Step S3, output an equipment configuration list including recommended photovoltaic installed capacity, energy storage system model and configuration quantity, inverter model and configuration quantity, as well as a typical cycle time-period operation strategy.

[0019] In step S2, the objective function of the mixed integer programming model is to minimize the ratio of the total system investment to the expected annual net income after the system is put into operation. The total investment includes the investment costs of photovoltaics, energy storage and inverters, and the annual net income is the difference in annual electricity expenditure before and after the system is put into operation.

[0020] The time-series data collected in step S1 includes: the unit photovoltaic installed capacity output curve characterizing the solar resource conditions, the load power curve characterizing the user's electricity consumption habits, and the grid electricity purchase price curve including the peak-valley electricity price difference.

[0021] The discrete decision variables in step S2 include: a binary variable for selecting the specific model of the energy storage device, an integer variable for determining the number of energy storage devices, a binary variable for selecting the specific model of the inverter, and an integer variable for determining the number of inverters; the continuous decision variables include: photovoltaic installed capacity, and the grid power purchase, energy storage charging and discharging power, energy storage power status, and photovoltaic curtailment power for each time period.

[0022] The constraints in step S2 include at least the following: system power generation and consumption balance constraints for each time period, upper limit constraints for photovoltaic installed capacity, constraints on the maximum output of photovoltaics by the total rated power of the inverter, constraints on the dynamic update of energy storage power status, constraints on the safe range of energy storage power, constraints on the upper and lower limits of energy storage charging and discharging power, and constraints on the mutual exclusion of energy storage charging and discharging behavior at the same time.

[0023] The optimal operating strategy obtained in step S3 provides specific power commands for grid power purchase, energy storage charging, and energy storage discharging for each time interval within a typical cycle, and ensures that the energy storage power status is consistent at the start and end of the cycle.

[0024] The output of step S4 also includes the total system investment, expected annual electricity savings, and core economic indicators such as static investment payback period, calculated based on optimal configuration and operation strategies.

[0025] One specific application of this embodiment is: Step S1, Data and Parameters Before initiating optimization calculations, the following key information needs to be systematically collected and organized as the basis for the model's input parameters and constraints: Time series data Define time resolution as (Typically 0.5 hours), a typical optimization cycle includes A time period, usually one week: ,in ; Photovoltaic power output curve: 30-minute intervals of photovoltaic power generation per kilowatt (unit: kW / kWp) in a typical meteorological year at the project site, denoted as... This fully depicts the photovoltaic power generation capacity of the region at different times of the day; Load power sequence: Based on historical electricity meter data, two typical load patterns are extracted: weekdays and holidays. Weekday load curve Holiday load curve (Unit: kW), totaled as ; Electricity purchase price sequence: Input the proposed 30-minute time-of-use electricity price for the future. (Unit: Yuan / kWh) The price differences between peak, flat and off-peak periods are the core driving factors for energy storage arbitrage and the economic operation of the system.

[0026] Equipment and Technical Parameters Photovoltaic modules: Comprehensive cost per unit capacity under current market conditions (Unit: RMB / kWp) Maximum photovoltaic installation capacity that can be accommodated on the roof or site (Unit: kWp); where With the fluctuation of photovoltaic module market prices, Limited by the available installation area on the roof.

[0027] Energy storage system: Defines the set of optional energy storage device models. For any model Key parameters required: single cabinet capacity (Unit: kWh / cabinet); Maximum charge / discharge power (Unit: kW / cabinet); Cost of purchase per cabinet (Unit: Yuan / cabinet).

[0028] Inverters: A collection of market-available models ,model corresponding rated power (Unit: kW) and Procurement Costs (Unit: Yuan)

[0029] Efficiency and lifespan parameters: Energy storage charging efficiency Discharge efficiency Photovoltaic inverter conversion efficiency The depth of charge / discharge (DOD) of the energy storage battery, where 0 ≥ DOD ≥ 1.

[0030] Step S2: Construct a mixed-integer programming model Step S21, Decision Variables Step S211: Design Selection Variables : A continuous variable representing the final recommended total photovoltaic installed capacity (unit: kWp), satisfying the constraint 0 ≤ ≤ .

[0031] : Select energy storage equipment model (0-1 variable). Indicates the selection of energy storage model And at most one model can be selected, and it is allowed to not configure energy storage, that is .

[0032] : The number of energy storage cabinets, a non-negative integer variable. The validity of these numbers is tied to the selected model k. When model k is selected, the total system energy is... The total power is

[0033] : 0-1 variables, representing the inverter selection result; =1 indicates that model i is selected. =0 indicates that no selection is made. This must be satisfied. .

[0034] : The number of inverters configured, a non-negative integer variable. When model i is selected, the total power of the system inverters is .

[0035] Step S212: Design runtime variables The power purchased by the power grid at time t (in kW) satisfies the constraints. ≥0; The energy storage charging power (unit: kW) at time t satisfies the constraints. ≥0; The energy storage discharge power (unit: kW) at time t satisfies the constraints. ≥0; SOC(t): The remaining energy capacity of the energy storage battery at the initial state at time t (unit: kWh), which satisfies the constraint SOC(t)≥0; : Photovoltaic curtailment power at time t (unit: kW), used to quantify the amount of electricity wasted when photovoltaic power generation exceeds load demand and energy storage charging capacity.

[0036] Step S22: Establish the objective function The static payback period is calculated as follows: Payback period (years) = Total investment / Annual net income; therefore, minimizing the payback period is equivalent to maximizing the ratio of annual net income to total investment. The objective function is set as follows:

[0037] in: Total investment

[0038]

[0039]

[0040] Annual net income ( ): Annual net income = Electricity cost of the previous year - Electricity cost of the new year; Original annual electricity bill: ; New year's electricity bill: ; Therefore, annual net income ; In summary, the objective function can be transformed into:

[0041] The numerator represents the total system investment, and the denominator represents the expected annual net income after the system is put into operation, which is the difference between the electricity cost in the previous year and the electricity cost in the new year. By solving the above objective function, the model automatically finds the balance point between the equipment combination and operating strategy that maximizes the return on investment, achieving the optimal balance between investment costs and annual income.

[0042] Step S23, Constraints Step S231, Power Balance Constraint At every time t, the total power supplied by all power sources equals the total power consumed by all power sources: power supplied by power sources includes photovoltaic power generation, energy storage discharge, and electricity purchased from the grid; power consumed by power sources includes electricity used by businesses, energy storage charging, and unused curtailed solar power.

[0043] The power supply side includes the effective photovoltaic power generation. Energy storage and discharge power Power purchased by the power grid The electricity consumption side includes the enterprise load power. Energy storage charging power Photovoltaic curtailment power .

[0044] Step S232, Inverter capacity constraint The inverter's output power must not exceed its rated power; that is, the maximum AC power after the photovoltaic DC power is converted by the inverter must be less than or equal to the inverter's rated power.

[0045] Step S233, Energy Storage System Operation Constraints Energy status update constraint: Current energy storage capacity = Previous energy storage capacity + Charge amount - Discharge amount.

[0046] Energy storage model and capacity correlation constraints: The total system capacity and total power are determined by the cabinet parameters and configuration quantity of the selected model.

[0047]

[0048] Battery capacity safety range constraints: The battery capacity must be maintained within a safe range to avoid overcharging or over-discharging. The usable range is determined by the depth of discharge (DOD).

[0049] Charge and discharge power limits: The charge and discharge power must not exceed the total power limit of the energy storage system.

[0050] Charge-discharge mutual exclusion constraint: introducing auxiliary variables Energy storage cannot be in both charging and discharging states at the same time. and M can be any positive integer, and it cannot be greater than 0 at the same time.

[0051]

[0052] Selection and Quantitative Logic Constraints: Ensure that the selected variable is valid only if and only if the selected variable is valid. or Only when the corresponding quantity variable is greater than 0 can M be any positive integer.

[0053]

[0054] Cycle consistency constraint: To ensure the sustainability of the operation strategy, the energy storage capacity at the beginning and end of a typical cycle must be consistent.

[0055]

[0056] Step S3: Solving the model Once the model is built, the configuration of the photovoltaic system is transformed into a standardized mixed-integer programming problem. Under the condition of satisfying the constraints, the objective function is solved using a professional mathematical optimization solver, and finally the global optimal solution or an approximate optimal solution that meets the engineering accuracy requirements can be obtained.

[0057] Step S4, Solution Output After the solution is obtained, the decision variables output a complete set of optimal technical implementation plans that can directly guide the project implementation and subsequent operation, specifically including: Step S41, Precise Equipment Configuration List Photovoltaic systems: Recommended installation capacity ; Energy storage system: the specific energy storage product model k* selected, and the quantity of energy storage cabinets to be purchased. Based on this, the total energy storage capacity is determined. With total power ; Inverter: The specific energy storage product model i* selected, and the number of units configured. Based on this, the total power of the inverter can be determined, or the conclusion that "it is more economical not to configure energy storage" can be drawn.

[0058] Step S42, Refined Time-by-Time Operation Strategy Table Output the operation strategy table and clarify the following core operation instructions: energy storage charging power during off-peak electricity price periods; power allocation strategy during midday peak photovoltaic output periods; energy storage discharge start-up time and discharge power during peak electricity price periods; and power supply sources during low-load periods at night.

[0059] This strategy table can be directly provided to energy management system (EMS) suppliers as the core configuration basis for their control strategy modules.

[0060] Step S43, Comprehensive Economic Analysis Report Based on the optimal configuration scheme, the following core economic indicators are output: estimated total system investment; Annual electricity cost savings forecast; core financial evaluation indicators, including dynamic payback period and internal rate of return, provide data support for investment decisions.

[0061] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0062] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for optimizing the configuration of a photovoltaic-storage system based on mixed-integer programming, characterized in that, The method includes the following steps: Step S1, Data Preparation: Collect photovoltaic output time-series data, user load time-series data, grid time-of-use electricity price time-series data, as well as equipment parameters and technical and economic parameters of photovoltaic modules, energy storage systems, and inverters at the project site; Step S2, Model Construction: Based on the data and parameters collected in Step S1, a mixed integer programming model is constructed with the goal of minimizing the static investment payback period of the system. This model includes discrete decision variables for equipment selection and capacity configuration, continuous decision variables for determining the system's time-period operating status, and constraints describing the physical operating laws of the system and the logic of equipment selection. Step S3, Model Solving: Input the mixed integer programming model constructed in step S2 into the mathematical optimization solver for solving to obtain the equipment configuration scheme that optimizes the objective function and the corresponding time-period operation strategy within a typical cycle; Step S4, Solution Output: Based on the optimal decision variable values ​​obtained in Step S3, output an equipment configuration list including recommended photovoltaic installed capacity, energy storage system model and configuration quantity, inverter model and configuration quantity, as well as a typical cycle time-period operation strategy.

2. The method for optimizing the configuration of a photovoltaic-storage system based on mixed-integer programming according to claim 1, characterized in that, In step S2, the objective function of the mixed integer programming model is to minimize the ratio of the total system investment to the expected annual net income after the system is put into operation. The total investment includes the investment costs of photovoltaics, energy storage and inverters, and the annual net income is the difference in annual electricity expenditure before and after the system is put into operation.

3. The method for optimizing the configuration of a photovoltaic-storage system based on mixed-integer programming according to claim 1, characterized in that, The time-series data collected in step S1 includes: the unit photovoltaic installed capacity output curve characterizing the solar resource conditions, the load power curve characterizing the user's electricity consumption habits, and the grid electricity purchase price curve including the peak-valley electricity price difference.

4. The method for optimizing the configuration of a photovoltaic-storage system based on mixed-integer programming according to claim 1, characterized in that, The discrete decision variables in step S2 include: a binary variable for selecting a specific model of energy storage equipment, an integer variable for determining the number of energy storage equipment to be configured, a binary variable for selecting a specific model of inverter, and an integer variable for determining the number of inverters to be configured; the continuous decision variables include: photovoltaic installed capacity, and the grid power purchase, energy storage charging and discharging power, energy storage power status, and photovoltaic curtailment power for each time period.

5. The method for optimizing the configuration of a photovoltaic-storage system based on mixed-integer programming according to claim 1, characterized in that, The constraints described in step S2 include at least the following: system power generation and consumption balance constraints for each time period, upper limit constraints for photovoltaic installed capacity, constraints on the maximum output of photovoltaics by the total rated power of the inverter, constraints on the dynamic update of energy storage power status, constraints on the safe range of energy storage power, constraints on the upper and lower limits of energy storage charging and discharging power, and constraints on the mutual exclusion of energy storage charging and discharging behavior at the same time.

6. The method for optimizing the configuration of a photovoltaic-storage system based on mixed-integer programming according to claim 1, characterized in that, The optimal operating strategy obtained in step S3 provides specific power commands for grid power purchase, energy storage charging, and energy storage discharging for each time interval within a typical cycle, and ensures that the energy storage power status is consistent at the start and end of the cycle.

7. The method for optimizing the configuration of a photovoltaic-storage system based on mixed-integer programming according to claim 1, characterized in that, The output of step S4 also includes the total system investment, expected annual electricity savings, and core economic indicators such as static investment payback period, calculated based on optimal configuration and operation strategies.

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