Multi-energy system optimization method and device, electronic equipment and storage medium

Abstract

The invention provides a multi-energy system optimization method and device, electronic equipment and a storage medium, and belongs to the technical field of linear programming, and the method comprises the steps: obtaining multi-energy system data, building a boundary constraint condition of a multi-energy system based on the multi-energy system data, obtaining power grid electricity price data, building a discrete time model based on the power grid electricity price data, and obtaining a discrete time model; the method comprises the steps of obtaining a discrete time model, obtaining a fixed coefficient of the multi-energy system based on the discrete time model, establishing an optimal income objective function based on the fixed coefficient of the multi-energy system, and solving the optimal income objective function based on a linear programming algorithm and a boundary constraint condition of the multi-energy system to obtain a multi-energy system optimization scheme. The technical problems that in the prior art, for a multi-energy system, calculation is complex, and income maximization cannot be achieved are solved.

Description

Technical Field

[0001] The present invention relates to the field of linear programming technology, and in particular to a multi-energy system optimization method, device, electronic equipment and storage medium. Background Art

[0002] Energy and environment are important material foundations for the survival and development of human society. The energy that humans currently rely on is fossil fuels. However, fossil energy is not only facing the risk of depletion, but also the environmental problems caused by the large-scale use of fossil fuels are seriously restricting the coordinated development of economy, energy and environment.

[0003] Therefore, in order to solve the above problems, a multi-energy system is proposed, which can improve the efficiency of social energy utilization, promote the large-scale development of renewable energy, improve the utilization rate of social infrastructure and energy supply security.

[0004] However, the existing technology for multi-energy systems has technical problems such as complex calculations and inability to achieve maximum benefits. Summary of the invention

[0005] In view of this, it is necessary to provide a multi-energy system optimization method, device, electronic device and storage medium to solve the technical problems in the prior art of multi-energy systems, such as complex calculations and inability to maximize benefits.

[0006] In order to solve the above technical problems, on the one hand, the present invention provides a multi-energy system optimization method, comprising: Obtain multi-energy system data; multi-energy system data includes: grid load power, energy storage capacity, energy storage power, charging pile power and photovoltaic equipment power; Based on the multi-energy system data, construct the boundary constraints of the multi-energy system; Obtaining grid electricity price data, establishing a discrete time model based on the grid electricity price data, and obtaining fixed coefficients of the multi-energy system based on the discrete time model; the fixed coefficients of the multi-energy system are: the time of the multi-energy system, constants in the linear constraints, and coefficients of the decision variables; Based on the fixed coefficients of the multi-energy system, the optimal benefit objective function is established; Based on the linear programming algorithm and the boundary constraints of the multi-energy system, the optimal benefit objective function is solved to obtain the optimization plan of the multi-energy system.

[0007] In a possible implementation, the boundary constraints of the multi-energy system include: Energy storage system capacity constraints, photovoltaic equipment power constraints and charging pile power constraints.

[0008] In a possible implementation, the energy storage system capacity constraint condition is:

[0009] in, For the The lower limit of the energy storage system For the The initial capacity of the energy storage system, is the power change value at the end of the next time period and For the The upper limit of the energy storage system.

[0010] In a possible implementation, the power grid constraint condition is:

[0011]

[0012] in: is the number of energy storage systems, is the initial energy storage cabinet, For the Energy storage cabinets in time period The power, is the maximum power limit allowed by the power grid, is the lower limit of the maximum power allowed by the power grid and is the load power currently measured by the power grid.

[0013] In a possible implementation, the photovoltaic device power constraint condition is:

[0014]

[0015] in, For the The power of each photovoltaic device in the current time period, For the The maximum power of each photovoltaic device and For the The minimum power of a photovoltaic device.

[0016] In a possible implementation, the charging pile power constraint condition is:

[0017]

[0018] in, For the The power of each charging pile in the current time period, For the The maximum power of each charging station and For the The minimum power of a charging station.

[0019] In a possible implementation, the optimal benefit objective function is:

[0020] in, To minimize the is the number of energy storage bins, is the number of time periods, The energy storage bin with index 0, is the time period with index 0, For the Energy storage system in time period The power, For the The electricity price for a period of time, is the length of the time period, is the number of PV systems, is the photovoltaic system with index 0, For the The power of a photovoltaic system in the current time period, is the initial electricity price, is the number of V2G or ordinary charging piles, For V2G or ordinary charging piles with index 0 and For the The power of a charging pile in the current time period.

[0021] On the other hand, the present invention also provides a multi-energy system optimization device, comprising: The acquisition module is used to obtain multi-energy system data; the multi-energy system data includes: grid load power, energy storage warehouse capacity, energy storage warehouse power, charging pile power and photovoltaic equipment power; Boundary constraint condition module, used to construct boundary constraint conditions of multi-energy system based on multi-energy system data; The fixed coefficient module is used to obtain the power grid price data, establish a discrete time model based on the power grid price data, and obtain the fixed coefficients of the multi-energy system based on the discrete time model; the fixed coefficients of the multi-energy system are: the time of the multi-energy system, the constants in the linear constraints and the coefficients of the decision variables; The optimal benefit objective function module is used to establish the optimal benefit objective function based on the fixed coefficients of the multi-energy system; The output module is used to solve the optimal benefit objective function based on the linear programming algorithm and the boundary constraints of the multi-energy system to obtain the optimization plan of the multi-energy system.

[0022] On the other hand, the present invention also provides an electronic device, including a memory and a processor, wherein: The memory is used to store programs; The processor is coupled to the memory and is used to execute the program stored in the memory to implement the steps in the multi-energy system optimization method in any of the above implementations.

[0023] On the other hand, the present invention also provides a computer-readable storage medium for storing computer-readable programs or instructions, which, when executed by a processor, can implement the steps of the multi-energy system optimization method described in any of the above-mentioned implementation methods.

[0024] The beneficial effects of the present invention are as follows: a multi-energy system optimization method provided by the present invention comprises: obtaining multi-energy system data, the multi-energy system data are: power grid load power, energy storage warehouse capacity, energy storage warehouse power, charging pile power and photovoltaic equipment power, and constructing boundary constraints of the multi-energy system based on the multi-energy system data, the purpose of which is to set reasonable boundary constraints of the multi-energy system; obtaining power grid electricity price data, establishing a discrete time model based on the power grid electricity price data, and obtaining fixed coefficients of the multi-energy system based on the discrete time model; the fixed coefficients of the multi-energy system are: the time of the multi-energy system, constants in the linear constraints and coefficients of the decision variables; based on the fixed coefficients of the multi-energy system Determine the coefficients and establish the optimal benefit objective function. The purpose of this is to determine the fixed coefficients of the multi-energy system based on the discrete time model. Based on the fixed coefficients, the optimal benefit objective function is established. The discrete time model is determined in order to be able to calculate the transformation of the multi-energy system and the dynamic modification of electricity prices in real time; based on the linear programming algorithm and the boundary constraints of the multi-energy system, the optimal benefit objective function is solved to obtain the optimization plan of the multi-energy system. The purpose of this is to dynamically optimize the multi-energy system by setting reasonable objective functions and constraints, and to find the optimal solution with the goal of maximizing benefits and minimizing costs, so as to achieve the purpose of maximizing benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use 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 work.

[0026] Figure 1 A schematic flow chart of an embodiment of the multi-energy system optimization method provided by the present invention; Figure 2 A wind-solar-diesel-storage-charging system architecture diagram of an embodiment of the multi-energy system optimization method provided by the present invention; Figure 3 A power line graph of hybrid energy storage, office building load and photovoltaics in one embodiment of the multi-energy system optimization method provided by the present invention; Figure 4 A power line graph of an office building in one embodiment of the multi-energy system optimization method provided by the present invention; Figure 5 A photovoltaic power line graph of an embodiment of the multi-energy system optimization method provided by the present invention; Figure 6 A line graph of the operating power of an energy storage cabinet of an embodiment of the multi-energy system optimization method provided by the present invention; Figure 7 A line graph of the power of the power grid after the office building load is regulated by energy storage and photovoltaics in one embodiment of the multi-energy system optimization method provided by the present invention; Figure 8 A schematic diagram of the structure of an embodiment of a multi-energy system optimization device provided by the present invention; Fig. 9 A schematic structural diagram of an embodiment of an electronic device provided by the present invention. DETAILED DESCRIPTION

[0027] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0028] In the description of the embodiments of the present application, unless otherwise specified, “plurality” means two or more than two.

[0029] The terms "including" and "having" and any variations thereof in the embodiments of the present invention are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product or equipment comprising a series of steps or modules is not necessarily limited to those steps or modules clearly listed, but may include other steps or modules not clearly listed or inherent to these processes, methods, products or equipment.

[0030] The naming or numbering of the steps in the embodiments of the present invention does not mean that the steps in the method flow must be executed in the time / logical sequence indicated by the naming or numbering. The execution order of the named or numbered process steps can be changed according to the technical purpose to be achieved, as long as the same or similar technical effects can be achieved.

[0031] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0032] The present invention provides a multi-energy system optimization method, device, electronic device and storage medium, which are described below respectively.

[0033] Figure 1 A schematic diagram of an embodiment of the multi-energy system optimization method provided by the present invention and Figure 2 The wind-solar-diesel-storage-charging system architecture diagram of an embodiment of the multi-energy system optimization method provided by the present invention includes: S101. Acquire multi-energy system data; the multi-energy system data includes: grid load power, energy storage warehouse capacity, energy storage warehouse power, charging pile power and photovoltaic equipment power; S102. Construct boundary constraints of the multi-energy system based on the multi-energy system data; S103, obtaining power grid price data, establishing a discrete time model based on the power grid price data, and obtaining fixed coefficients of the multi-energy system based on the discrete time model; the fixed coefficients of the multi-energy system are: the time of the multi-energy system, constants in the linear constraints, and coefficients of the decision variables; S104. Establishing an optimal benefit objective function based on fixed coefficients of the multi-energy system; S105. Solve the optimal benefit objective function based on the linear programming algorithm and the boundary constraints of the multi-energy system to obtain an optimization solution for the multi-energy system.

[0034] It can be understood that the multi-energy system optimization method proposed in the present invention includes the following steps: (1) First, according to the upper and lower limits of grid load power, the capacity and power of energy storage warehouses, the upper and lower limits of charging pile power, and the upper and lower limits of photovoltaic system power, the boundary constraints of the system decision variables are obtained. Then, according to the grid electricity price, a discrete time model is established to obtain the fixed coefficients of the system. Finally, according to the constraints contained in the system decision variables obtained above, the fixed coefficients of the system, and the optimal profit objective function, the data value set of the system decision variables is obtained, thereby effectively achieving the effect of maximum profit planning.

[0035] (2) Variable definition: : The number of energy storage systems, that is, the number of energy storage bins, energy storage battery bins and energy storage cabinets.

[0036] n : The number of time periods, which represents the number of time intervals divided according to the regional peak and valley electricity prices in the optimization problem. In this design, the sample data is designed after comprehensively considering the peak and valley electricity prices of provinces, municipalities and autonomous regions across the country, with a duration of half an hour, and a total of 48 time periods throughout the day.

[0037] p : Number of photovoltaic systems.

[0038] cp : The number of V2G or ordinary charging piles.

[0039] c : An array of electricity prices for each time period.

[0040] : No. Energy storage system in time period Power (kW).

[0041] : No. The amount of energy storage system (kWh).

[0042] : No. The maximum capacity of the energy storage system (kWh).

[0043] : No. The minimum capacity of an energy storage system (kWh).

[0044] : No. The initial capacity of the energy storage system (kWh).

[0045] : Maximum power allowed by the power grid (kW).

[0046] : Minimum power allowed by the power grid (kW).

[0047] : The current real-time power of the power grid (kW).

[0048] : No. The electricity price for a period of time.

[0049] : The length of the time period.

[0050] (3) Formula derivation: 1. Yes Energy storage battery compartment, each energy storage battery compartment The upper limit of capacity is , the lower capacity limit is .

[0051] 2. Starting from zero hour, there are time period, set (half an hour is a section), divided into 48 sections, that is, the daily section electricity price: .

[0052] 3. The energy storage battery compartment is charged and discharged in 48 intervals per day. Power .for , when the energy storage battery compartment is charging, , the grid load increases, and the energy storage battery compartment discharges. , the grid load is reduced, and .

[0053] 4. Yes Each photovoltaic carport The real-time power at the current time is , the upper limit of photovoltaic power generation is , the lower limit of photovoltaic power generation is .and When photovoltaic power generation occurs, the load on the grid decreases.

[0054] 5. Yes Charging piles, each charging pile The real-time power at the current time is , the upper limit of charging pile power is , the lower limit of photovoltaic power generation is ,and .when When , the charging pile is a common charging pile. When the charging pile power is positive, the car is charged and the grid load increases. When the charging pile power is negative, the car is discharged and the grid load decreases.

[0055] 6. The goal is to minimize the total cost and maximize the benefits in the next 24 hours, including the charging and discharging costs of energy storage equipment, the benefits brought by photovoltaic power generation, and the charging profits of charging piles, specifically:

[0056] in, To minimize the is the number of energy storage bins, is the number of time periods, The energy storage bin with index 0, is the time period with index 0, For the Energy storage system in time period The power, For the The electricity price for a period of time, is the length of the time period, is the number of PV systems, is the photovoltaic system with index 0, For the The power of a photovoltaic system in the current time period, is the initial electricity price, is the number of V2G or ordinary charging piles, For V2G or ordinary charging piles with index 0 and For the The power of each charging pile in the current time period is summed starting from the i=0, j=0, k=0, l=0 items, and continuing to the m-1, n, p-1, and cp-1 items.

[0057] 7. Existence constraints 7.1 Energy Storage System Capacity Constraints The energy storage device power constraint ensures that the power of each energy storage device is within its upper and lower limits to avoid overcharging or over-discharging. Energy storage system, the power change is:

[0058] The corresponding constraints are:

[0059] Split into inequalities:

[0060]

[0061] in: It is The initial capacity of the energy storage system, It is The upper limit of the energy storage system It is The lower limit of the energy storage system.

[0062] 7.2 Total Power Constraints The total power needs to be divided into two parts: the current time period and the future time period.

[0063] Current time period: The total power constraint for the current time period includes all devices, ensuring that the system power in the current time period does not exceed the upper and lower limits of the grid:

[0064]

[0065] Future time period: The total power constraint in the future time period only considers the energy storage equipment to ensure that the power of the system in the future time period does not exceed the upper and lower limits of the power grid:

[0066]

[0067] in: is the maximum power limit allowed by the power grid, is the lower limit of the maximum power allowed by the power grid and is the load power currently measured by the power grid.

[0068] 7.3 Constraints on equal power in time periods with the same electricity price If the electricity price is the same in two consecutive time periods, the power of the energy storage device in these two time periods should be equal to avoid invalid charging and discharging operations. This constraint avoids invalid charging and discharging operations in time periods with the same electricity price, which has a positive effect on extending battery life: When present:

[0069] have: .

[0070] 7.4 Power Constraints of Photovoltaic Equipment The power of a PV system must be within its upper and lower limits to ensure that the power generation of the PV system does not exceed its physical limitations:

[0071]

[0072] in: It is The maximum power of each photovoltaic device and It is The minimum power of a photovoltaic device.

[0073] 7.5 Power Constraints of Charging Pile The power of the charging pile must be between its upper and lower limits to ensure that the charging power of the charging pile does not exceed its physical limitations:

[0074]

[0075] in: It is The maximum power of each charging station and It is The minimum power of a charging station.

[0076] For constraints: For a single energy storage bin, at any time For the current period, This means that this is a multidimensional It changes with the calling time. The planning is done at intervals of 10 seconds, because It may be a decimal number. Charge and Discharge (ceil( )- ). Hours, and then the normal half-hour switch charging and discharging power until After finishing, restart Charge and Discharge ( -floor( )). Hours of time. For example, , first round it down floor( ) , calculate the current tiered electricity price for:

[0077] Updated by this , with a length of 49.

[0078] Battery level is , the power at the end of any time period is have

[0079] Combining the electricity at the end of all time periods, we get the complete form of constraint 2:

[0080] in, For the i The lower limit of the energy storage system For the i The initial capacity of the energy storage system, is the length of the time period, Round to the current time period. is the current time, is the power of the initial energy storage battery compartment, is the power of the first energy storage bin, The power of the 48th energy storage bin and For the i The upper limit of the energy storage system.

[0081] When the number of energy storage warehouses is expanded to multiple, the power and power constraints of each energy storage device can be used as a dimension.

[0082]

[0083] The above is a multivariable model with a linear relationship. The solution of the model can be obtained by solving the model. Currently, open source programming languages ​​​​Python, C or mathematical tools MATLAB can be directly used to solve such problems. You only need to follow their writing rules and input the values ​​​​of the above variables for specific problems to quickly get the solution results.

[0084] (4) After obtaining the optimal solution corresponding to the optimal objective function , , Afterwards, the power at the current first moment is taken as the expected value of the energy storage system during actual operation.

[0085] (5) By measuring the above power values ​​at the PV storage and charging sites, the planning will be triggered again when the values ​​do not meet the expectations.

[0086] It can be further understood that the multi-energy system optimization method proposed in the present invention has the following advantages: 1. The algorithm is lightweight. The number of decision variables is the number of energy storage cabinets m multiplied by the length of the electricity price array c plus the number of photovoltaic , plus the number of charging piles , small amount of calculation, can run in a low-power embedded environment, and quickly solve in milliseconds.

[0087] 2. Not affected by network factors, maximizing the stability of microgrid operation.

[0088] 3. The power constraint of the charging pile realizes intelligent and orderly charging. When the lower power limit is negative, the V2G function can be activated to achieve vehicle-grid complementarity.

[0089] 4. In the power constraint of the power grid, when the power lower limit is negative, power can be fed to the large power grid to achieve peak-valley arbitrage.

[0090] 5. It can calculate the changes of photovoltaic system in real time and absorb the maximum amount of green electricity.

[0091] 6. The maximum benefit will be calculated only when the system security is guaranteed.

[0092] 7.Electricity prices are modified dynamically, and operation and maintenance are simple and efficient.

[0093] It can be further understood that the specific implementation steps of the present invention are as follows: Step 1: Site layout.

[0094] In order to ensure the reliability of the algorithm, the data used in the algorithm are all from our solar energy storage charging demonstration station project. The charging piles use our self-developed 22KW AC pile, two 7KW AC piles, one 60KW DC pile, one 120KW liquid-cooled supercharger, and two 720KW pantographs. Figure 1 As shown in the figure, the main parameter acquisition frequency of each device data is 1Hz.

[0095] Step 2: Collect key data for preprocessing.

[0096] ① Obtain the real-time power and electricity information of the energy storage warehouse.

[0097] ② Obtain the real-time power of the power grid.

[0098] ③ Obtain the expected power, limited power, and real-time power of each charging pile.

[0099] ④ Obtain photovoltaic power generation power and power limit.

[0100] Step 3: Start the calculation based on whether the conditions for triggering the calculation are met.

[0101] ① When the power collected by the grid exceeds the upper or lower limit of the grid quota, it is triggered to ensure that the grid will not be overloaded or fed back.

[0102] ② When the photovoltaic power is not triggered when the photovoltaic power generation upper limit, the photovoltaic utilization rate is maximized.

[0103] ③ Triggered when the expected power of the charging pile changes to ensure maximum charging benefits.

[0104] ④ Triggered when electricity prices change to ensure maximum peak-valley arbitrage benefits.

[0105] ⑤Optional timing trigger.

[0106] It can be further understood that first, the boundary constraints of the system decision variables are obtained according to the upper and lower limits of the power of the power grid load, the capacity and power of the energy storage warehouse, the upper and lower limits of the power of the charging pile, and the upper and lower limits of the power of the photovoltaic system. Then, according to the power price of the power grid, a discrete time model is established to obtain the fixed coefficients of the system. Finally, according to the constraints contained in the system decision variables obtained above, the fixed coefficients of the system, and the optimal profit objective function, the data value set of the system decision variables is obtained, thereby effectively achieving the effect of maximum profit planning.

[0107] Figure 3 A power line chart of hybrid energy storage, office building load and photovoltaic power for an embodiment of the multi-energy system optimization method provided by the present invention, Figure 4 A power line chart of an office building in accordance with an embodiment of the multi-energy system optimization method provided by the present invention, Figure 5 A photovoltaic power line chart of an embodiment of the multi-energy system optimization method provided by the present invention, Figure 6 The operating power line graph of the energy storage cabinet of one embodiment of the multi-energy system optimization method provided by the present invention and Figure 7 A line graph of the power of the power grid of an office building load after energy storage and photovoltaic regulation in one embodiment of the multi-energy system optimization method provided by the present invention includes: Energy storage system capacity constraints, photovoltaic equipment power constraints and charging pile power constraints.

[0108] It is understood that the constraints are as follows: Energy storage system capacity constraints The energy storage device power constraint ensures that the power of each energy storage device is within its upper and lower limits to avoid overcharging or over-discharging. Energy storage system, the power change is:

[0109] The corresponding constraints are:

[0110] Split into inequalities:

[0111]

[0112] in: It is The initial capacity of the energy storage system, It is The upper limit of the energy storage system It is The lower limit of the energy storage system.

[0113] Total power constraint The total power needs to be divided into two parts: the current time period and the future time period.

[0114] Current time period: The total power constraint for the current time period includes all devices, ensuring that the system power in the current time period does not exceed the upper and lower limits of the grid:

[0115]

[0116] Future time period: The total power constraint in the future time period only considers the energy storage equipment to ensure that the power of the system in the future time period does not exceed the upper and lower limits of the power grid:

[0117]

[0118] in: is the maximum power limit allowed by the power grid, is the lower limit of the maximum power allowed by the power grid and is the load power currently measured by the power grid.

[0119] Constraints to keep power equal during time periods with the same electricity price If the electricity price is the same in two consecutive time periods, the power of the energy storage device in these two time periods should be equal to avoid invalid charging and discharging operations. This constraint avoids invalid charging and discharging operations in time periods with the same electricity price, which has a positive effect on extending battery life: When present:

[0120] have: .

[0121] Power Constraints of Photovoltaic Installations The power of a PV system must be within its upper and lower limits to ensure that the power generation of the PV system does not exceed its physical limitations:

[0122]

[0123] in: It is The maximum power of each photovoltaic device and It is The minimum power of a photovoltaic device.

[0124] Power constraints of charging piles The power of the charging pile must be between its upper and lower limits to ensure that the charging power of the charging pile does not exceed its physical limitations:

[0125]

[0126] in: It is The maximum power of each charging station and It is The minimum power of a charging station.

[0127] For constraints: For a single energy storage bin, at any time For the current period, This means that this is a multidimensional It changes with the calling time. The planning is done at intervals of 10 seconds, because It may be a decimal number. Charge and Discharge (ceil( )- ). Hours, and then the normal half-hour switch charging and discharging power until After finishing, restart Charge and Discharge ( -floor( )). Hours of time. For example, , first round it down floor( ) , calculate the current tiered electricity price for:

[0128] Updated by this , with a length of 49.

[0129] Battery level is , the power at the end of any time period is have

[0130] Combining the electricity at the end of all time periods, we get the complete form of constraint 2:

[0131] in, For the i The lower limit of the energy storage system For the i The initial capacity of the energy storage system, is the length of the time period, Round to the current time period. is the current time, is the power of the initial energy storage battery compartment, is the power of the first energy storage bin, The power of the 48th energy storage bin and For the i The upper limit of the energy storage system.

[0132] When the number of energy storage warehouses is expanded to multiple, the power and power constraints of each energy storage device can be used as a dimension.

[0133]

[0134] The above is a multivariable model with a linear relationship. The solution of the model can be obtained by solving the model. Currently, open source programming languages ​​​​Python, C or mathematical tools MATLAB can be directly used to solve such problems. You only need to follow their writing rules and input the values ​​​​of the above variables for specific problems to quickly get the solution results.

[0135] In some embodiments of the present invention, the energy storage system capacity constraint condition is:

[0136] in, For the The lower limit of the energy storage system For the The initial capacity of the energy storage system, is the power change value at the end of the next time period and For the The upper limit of the energy storage system.

[0137] It is understood that the constraints are as follows: Energy storage system capacity constraints The energy storage device power constraint ensures that the power of each energy storage device is within its upper and lower limits to avoid overcharging or over-discharging. Energy storage system, the power change is:

[0138] The corresponding constraints are:

[0139] Split into inequalities:

[0140]

[0141] in: It is The initial capacity of the energy storage system, It is The upper limit of the energy storage system It is The lower limit of the energy storage system.

[0142] Total power constraint The total power needs to be divided into two parts: the current time period and the future time period.

[0143] Current time period: The total power constraint for the current time period includes all devices, ensuring that the system power in the current time period does not exceed the upper and lower limits of the grid:

[0144]

[0145] Future time period: The total power constraint in the future time period only considers the energy storage equipment to ensure that the power of the system in the future time period does not exceed the upper and lower limits of the power grid:

[0146]

[0147] in: is the maximum power limit allowed by the power grid, is the lower limit of the maximum power allowed by the power grid and is the load power currently measured by the power grid.

[0148] Constraints to keep power equal during time periods with the same electricity price If the electricity price is the same in two consecutive time periods, the power of the energy storage device in these two time periods should be equal to avoid invalid charging and discharging operations. The constraint avoids invalid charging and discharging operations in time periods with the same electricity price, which has a positive effect on extending battery life: When present:

[0149] have: .

[0150] Power Constraints of Photovoltaic Installations The power of a PV system must be within its upper and lower limits to ensure that the power generation of the PV system does not exceed its physical limitations:

[0151]

[0152] in: It is The maximum power of each photovoltaic device and It is The minimum power of a photovoltaic device.

[0153] Power constraints of charging piles: The power of the charging pile must be between its upper and lower limits to ensure that the charging power of the charging pile does not exceed its physical limitations:

[0154]

[0155] in: It is The maximum power of each charging station and It is The minimum power of a charging station.

[0156] For constraints: For a single energy storage bin, at any time For the current period, This means that this is a multidimensional It changes with the calling time. The planning is done at intervals of 10 seconds, because It may be a decimal number. Charge and Discharge (ceil( )- ). Hours, and then the normal half-hour switch charging and discharging power until After finishing, restart Charge and Discharge ( -floor( )). Hours of time. For example, , first round it down floor( ) , calculate the current tiered electricity price for:

[0157] Updated by this , with a length of 49.

[0158] Battery level is , the power at the end of any time period is have

[0159] Combining the electricity at the end of all time periods, we get the complete form of constraint 2:

[0160] in, For the i The lower limit of the energy storage system For the i The initial capacity of the energy storage system, is the length of the time period, Round to the current time period. is the current time, is the power of the initial energy storage battery compartment, is the power of the first energy storage bin, The power of the 48th energy storage bin and For the i The upper limit of the energy storage system.

[0161] When the number of energy storage warehouses is expanded to multiple, the power and power constraints of each energy storage device can be used as a dimension.

[0162]

[0163] The above is a multivariable model with a linear relationship. The solution of the model can be obtained by solving the model. Currently, open source programming languages ​​​​Python, C or mathematical tools MATLAB can be directly used to solve such problems. You only need to follow their writing rules and input the values ​​​​of the above variables for specific problems to quickly get the solution results.

[0164] In some embodiments of the present invention, the grid constraint condition is:

[0165]

[0166] in: is the number of energy storage systems, is the initial energy storage cabinet, For the Energy storage cabinets in time period The power, is the maximum power limit allowed by the power grid, is the lower limit of the maximum power allowed by the power grid and It is the load power currently measured by the power grid. The purpose is to ensure that the power of all energy storage cabinets in the same time period does not exceed the upper and lower limits of the power grid.

[0167] It is understood that the constraints are as follows: Energy storage system capacity constraints The energy storage device power constraint ensures that the power of each energy storage device is within its upper and lower limits to avoid overcharging or over-discharging. Energy storage system, the power change is:

[0168] The corresponding constraints are:

[0169] Split into inequalities:

[0170]

[0171] in: It is The initial capacity of the energy storage system, It is The upper limit of the energy storage system It is The lower limit of the energy storage system.

[0172] Total power constraint The total power needs to be divided into two parts: the current time period and the future time period.

[0173] Current time period: The total power constraint for the current time period includes all devices, ensuring that the system power in the current time period does not exceed the upper and lower limits of the grid:

[0174]

[0175] Future time period: The total power constraint in the future time period only considers the energy storage equipment to ensure that the power of the system in the future time period does not exceed the upper and lower limits of the power grid:

[0176]

[0177] in: is the maximum power limit allowed by the power grid, is the lower limit of the maximum power allowed by the power grid and is the load power currently measured by the power grid.

[0178] Constraints to keep power equal during time periods with the same electricity price If the electricity price is the same in two consecutive time periods, the power of the energy storage device in these two time periods should be equal to avoid invalid charging and discharging operations. The constraint avoids invalid charging and discharging operations in time periods with the same electricity price, which has a positive effect on extending battery life: When present:

[0179] have: .

[0180] Power Constraints of Photovoltaic Installations The power of a PV system must be within its upper and lower limits to ensure that the power generation of the PV system does not exceed its physical limitations:

[0181]

[0182] in: It is The maximum power of each photovoltaic device and It is The minimum power of a photovoltaic device.

[0183] Power constraints of charging piles The power of the charging pile must be between its upper and lower limits to ensure that the charging power of the charging pile does not exceed its physical limitations:

[0184]

[0185] in: It is The maximum power of each charging station and It is The minimum power of a charging station.

[0186] For constraints: For a single energy storage bin, at any time For the current period, This means that this is a multidimensional It changes with the calling time. The planning is done at intervals of 10 seconds, because It may be a decimal number. Charge and Discharge (ceil( )- ). Hours, and then the normal half-hour switch charging and discharging power until After finishing, restart Charge and Discharge ( -floor( )). Hours of time. For example, , first round it down floor( ) , calculate the current tiered electricity price for:

[0187] Updated by this , with a length of 49.

[0188] Battery level is , the power at the end of any time period is have

[0189] Combining the electricity at the end of all time periods, we get the complete form of constraint 2:

[0190] in, For the i The lower limit of the energy storage system For the i The initial capacity of the energy storage system, is the length of the time period, Round to the current time period. is the current time, is the power of the initial energy storage battery compartment, is the power of the first energy storage bin, The power of the 48th energy storage bin and For the i The upper limit of the energy storage system.

[0191] When the number of energy storage warehouses is expanded to multiple, the power and power constraints of each energy storage device can be used as a dimension.

[0192]

[0193] The above is a multivariable model with a linear relationship. The solution of the model can be obtained by solving the model. Currently, open source programming languages ​​​​Python, C or mathematical tools MATLAB can be directly used to solve such problems. You only need to follow their writing rules and input the values ​​​​of the above variables for specific problems to quickly get the solution results.

[0194] In some embodiments of the present invention, the photovoltaic device power constraint condition is:

[0195]

[0196] in, For the The power of each photovoltaic device in the current time period, For the The maximum power of each photovoltaic device and For the The minimum power of a photovoltaic device.

[0197] It is understood that the constraints are as follows: Energy storage system capacity constraints The energy storage device power constraint ensures that the power of each energy storage device is within its upper and lower limits to avoid overcharging or over-discharging. Energy storage system, the power change is:

[0198] The corresponding constraints are:

[0199] Split into inequalities:

[0200]

[0201] in: It is The initial capacity of the energy storage system, It is The upper limit of the energy storage system It is The lower limit of the energy storage system.

[0202] Total power constraint The total power needs to be divided into two parts: the current time period and the future time period.

[0203] Current time period: The total power constraint for the current time period includes all devices, ensuring that the system power in the current time period does not exceed the upper and lower limits of the grid:

[0204]

[0205] Future time period: The total power constraint in the future time period only considers the energy storage equipment to ensure that the power of the system in the future time period does not exceed the upper and lower limits of the power grid:

[0206]

[0207] in: is the maximum power limit allowed by the power grid, is the lower limit of the maximum power allowed by the power grid and is the load power currently measured by the power grid.

[0208] Constraints to keep power equal during time periods with the same electricity price If the electricity price is the same in two consecutive time periods, the power of the energy storage device in these two time periods should be equal to avoid invalid charging and discharging operations. The constraint avoids invalid charging and discharging operations in time periods with the same electricity price, which has a positive effect on extending battery life: When present:

[0209] have: .

[0210] Power Constraints of Photovoltaic Installations The power of a PV system must be within its upper and lower limits to ensure that the power generation of the PV system does not exceed its physical limitations:

[0211]

[0212] in: It is The maximum power of each photovoltaic device and It is The minimum power of a photovoltaic device.

[0213] Power constraints of charging piles The power of the charging pile must be between its upper and lower limits to ensure that the charging power of the charging pile does not exceed its physical limitations:

[0214]

[0215] in: It is The maximum power of each charging station and It is The minimum power of a charging station.

[0216] For constraints: For a single energy storage bin, at any time For the current period, This means that this is a multidimensional It changes with the calling time. The planning is done at intervals of 10 seconds, because It may be a decimal number. Charge and Discharge (ceil( )- ). Hours, and then the normal half-hour switch charging and discharging power until After finishing, restart Charge and Discharge ( -floor( )). Hours of time. For example, , first round it down floor( ) , calculate the current tiered electricity price for:

[0217] Updated by this , with a length of 49.

[0218] Battery level is , the power at the end of any time period is have

[0219] Combining the electricity at the end of all time periods, we get the complete form of constraint 2:

[0220] in, For the i The lower limit of the energy storage system For the i The initial capacity of the energy storage system, is the length of the time period, Round to the current time period. is the current time, is the power of the initial energy storage battery compartment, is the power of the first energy storage bin, The power of the 48th energy storage bin and For the i The upper limit of the energy storage system.

[0221] When the number of energy storage warehouses is expanded to multiple, the power and power constraints of each energy storage device can be used as a dimension.

[0222]

[0223] The above is a multivariable model with a linear relationship. The solution of the model can be obtained by solving the model. Currently, open source programming languages ​​​​Python, C or mathematical tools MATLAB can be directly used to solve such problems. You only need to follow their writing rules and input the values ​​​​of the above variables for specific problems to quickly get the solution results.

[0224] In some embodiments of the present invention, the charging pile power constraint condition is:

[0225]

[0226] in, For the The power of each charging pile in the current time period, For the The maximum power of each charging station and For the The minimum power of a charging station.

[0227] It is understood that the constraints are as follows: Energy storage system capacity constraints The energy storage device power constraint ensures that the power of each energy storage device is within its upper and lower limits to avoid overcharging or over-discharging. Energy storage system, the power change is:

[0228] The corresponding constraints are:

[0229] Split into inequalities:

[0230]

[0231] in: It is The initial capacity of the energy storage system, It is The upper limit of the energy storage system It is The lower limit of the energy storage system.

[0232] Total power constraint The total power needs to be divided into two parts: the current time period and the future time period.

[0233] Current time period: The total power constraint for the current time period includes all devices, ensuring that the system power in the current time period does not exceed the upper and lower limits of the grid:

[0234]

[0235] Future time period: The total power constraint in the future time period only considers the energy storage equipment to ensure that the power of the system in the future time period does not exceed the upper and lower limits of the power grid:

[0236]

[0237] in: is the maximum power limit allowed by the power grid, is the lower limit of the maximum power allowed by the power grid and is the load power currently measured by the power grid.

[0238] Constraints to keep power equal during time periods with the same electricity price If the electricity price is the same in two consecutive time periods, the power of the energy storage device in these two time periods should be equal to avoid invalid charging and discharging operations. The constraint avoids invalid charging and discharging operations in time periods with the same electricity price, which has a positive effect on extending battery life: When present:

[0239] have: .

[0240] Power Constraints of Photovoltaic Installations The power of a PV system must be within its upper and lower limits to ensure that the power generation of the PV system does not exceed its physical limitations:

[0241]

[0242] in: It is The maximum power of each photovoltaic device and It is The minimum power of a photovoltaic device.

[0243] Power constraints of charging piles The power of the charging pile must be between its upper and lower limits to ensure that the charging power of the charging pile does not exceed its physical limitations:

[0244]

[0245] in: It is The maximum power of each charging station and It is The minimum power of a charging station.

[0246] For constraints: For a single energy storage bin, at any time For the current period, This means that this is a multidimensional It changes with the calling time. The planning is done at intervals of 10 seconds, because It may be a decimal number. Charge and Discharge (ceil( )- ). Hours, and then the normal half-hour switch charging and discharging power until After finishing, restart Charge and Discharge ( -floor( )). Hours of time. For example, , first round it down floor( ) , calculate the current tiered electricity price for:

[0247] Updated by this , with a length of 49.

[0248] Battery level is , the power at the end of any time period is have

[0249] Combining the electricity at the end of all time periods, we get the complete form of constraint 2:

[0250] in, For the i The lower limit of the energy storage system For the i The initial capacity of the energy storage system, is the length of the time period, Round to the current time period. is the current time, is the power of the initial energy storage battery compartment, is the power of the first energy storage bin, The power of the 48th energy storage bin and For the i The upper limit of the energy storage system.

[0251] When the number of energy storage warehouses is expanded to multiple, the power and power constraints of each energy storage device can be used as a dimension.

[0252]

[0253] The above is a multivariable model with a linear relationship. The solution of the model can be obtained by solving the model. Currently, open source programming languages ​​​​Python, C or mathematical tools MATLAB can be directly used to solve such problems. You only need to follow their writing rules and input the values ​​​​of the above variables for specific problems to quickly get the solution results.

[0254] In some embodiments of the present invention, the optimal profit objective function is:

[0255] in, To minimize the is the number of energy storage bins, is the number of time periods, The energy storage bin with index 0, is the time period with index 0, For the Energy storage system in time period The power, For the The electricity price for a period of time, is the length of the time period, is the number of PV systems, is the photovoltaic system with index 0, For the The power of a photovoltaic system in the current time period, is the initial electricity price, is the number of V2G or ordinary charging piles, For V2G or ordinary charging piles with index 0 and For the The power of each charging pile in the current time period is summed starting from the i=0, j=0, k=0, l=0 items, and continuing to the m-1, n, p-1, and cp-1 items.

[0256] Figure 8 A schematic diagram of the structure of an embodiment of a multi-energy system optimization device provided by the present invention includes: The acquisition module 801 is used to acquire multi-energy system data; the multi-energy system data includes: grid load power, energy storage warehouse capacity, energy storage warehouse power, charging pile power and photovoltaic equipment power; The boundary constraint module 802 is used to construct boundary constraints of the multi-energy system based on the multi-energy system data; The fixed coefficient module 803 is used to obtain the power grid price data, establish a discrete time model based on the power grid price data, and obtain the fixed coefficients of the multi-energy system based on the discrete time model; the fixed coefficients of the multi-energy system are: the time of the multi-energy system, the constants in the linear constraints and the coefficients of the decision variables; The optimal benefit objective function module 804 is used to establish an optimal benefit objective function based on the fixed coefficients of the multi-energy system; The output module 805 is used to solve the optimal benefit objective function based on the linear programming algorithm and the boundary constraints of the multi-energy system to obtain the multi-energy system optimization solution.

[0257] The multi-energy system optimization device 800 provided in the above embodiment can implement the technical solution described in the above multi-energy system optimization method embodiment. The specific implementation principles of the above modules or units can refer to the corresponding contents in the above multi-energy system optimization method embodiment, which will not be repeated here.

[0258] like Fig. 9 As shown, the present invention also provides an electronic device 900. The electronic device 900 includes a processor 901, a memory 902 and a display 903. Fig. 9 Only some components of the electronic device 900 are shown, but it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0259] In some embodiments, the processor 901 may be a central processing unit (CPU), a microprocessor or other data processing chip, used to run program codes or process data stored in the memory 902, such as the multi-energy system optimization method of the present invention.

[0260] In some embodiments, the processor 901 may be a single server or a server group. The server group may be centralized or distributed. In some embodiments, the processor 901 may be local or remote. In some embodiments, the processor 901 may be implemented in a cloud platform. In one embodiment, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-cloud, etc., or any combination thereof.

[0261] In some embodiments, the memory 902 may be an internal storage unit of the electronic device 900, such as a hard disk or memory of the electronic device 900. In other embodiments, the memory 902 may also be an external storage device of the electronic device 900, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the electronic device 900.

[0262] Furthermore, the memory 902 may include both an internal storage unit of the electronic device 900 and an external storage device. The memory 902 is used to store application software installed in the electronic device 900 and various data.

[0263] In some embodiments, the display 903 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, etc. The display 903 is used to display information on the electronic device 900 and to display a visual user interface. The components 901-903 of the electronic device 900 communicate with each other via a system bus.

[0264] In one embodiment, when the processor 901 executes the multi-energy system optimization program in the memory 902, the following steps may be implemented: Obtain multi-energy system data; multi-energy system data includes: grid load power, energy storage capacity, energy storage power, charging pile power and photovoltaic equipment power; Based on the multi-energy system data, construct the boundary constraints of the multi-energy system; Obtaining grid electricity price data, establishing a discrete time model based on the grid electricity price data, and obtaining fixed coefficients of the multi-energy system based on the discrete time model; the fixed coefficients of the multi-energy system are: the time of the multi-energy system, constants in the linear constraints, and coefficients of the decision variables; Based on the fixed coefficients of the multi-energy system, the optimal benefit objective function is established; Based on the linear programming algorithm and the boundary constraints of the multi-energy system, the optimal benefit objective function is solved to obtain the optimization plan of the multi-energy system.

[0265] It should be understood that: when the processor 901 executes the multi-energy system optimization program in the memory 902, in addition to the above functions, other functions can also be implemented. For details, please refer to the description of the corresponding method embodiment above.

[0266] Furthermore, the embodiment of the present invention does not specifically limit the type of the electronic device 900 mentioned, and the electronic device 900 may be a portable electronic device such as a mobile phone, a tablet computer, a personal digital assistant (PDA), a wearable device, a laptop computer, etc. Exemplary embodiments of portable electronic devices include but are not limited to portable electronic devices equipped with IOS, Android, Microsoft or other operating systems. The above-mentioned portable electronic devices may also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the present invention, the electronic device 900 may not be a portable electronic device, but a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0267] Accordingly, an embodiment of the present application also provides a computer-readable storage medium, which is used to store computer-readable programs or instructions. When the program or instructions are executed by a processor, it can implement the steps or functions in the multi-energy system optimization method provided by the above-mentioned method embodiments.

[0268] Those skilled in the art will appreciate that all or part of the processes of the above-mentioned embodiments can be implemented by instructing related hardware (such as a processor, a controller, etc.) through a computer program, and the computer program can be stored in a computer-readable storage medium, wherein the computer-readable storage medium is a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0269] The multi-energy system optimization method, device, electronic device and storage medium provided by the present invention are introduced in detail above. The principles and implementation methods of the present invention are explained in this article using specific examples. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for technical personnel in this field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A multi-energy system optimization method, characterized in that: include: Acquire multi-energy system data; The multi-energy system data are: grid load power, energy storage capacity, energy storage power, charging pile power and photovoltaic equipment power; Based on the multi-energy system data, construct the boundary constraints of the multi-energy system; Obtaining grid electricity price data, establishing a discrete time model based on the grid electricity price data, and obtaining fixed coefficients of the multi-energy system based on the discrete time model; the fixed coefficients of the multi-energy system are: the time of the multi-energy system, constants in the linear constraints, and coefficients of the decision variables; Based on the fixed coefficients of the multi-energy system, the optimal benefit objective function is established; Based on the linear programming algorithm and the boundary constraints of the multi-energy system, the optimal benefit objective function is solved to obtain the optimization plan of the multi-energy system.

2. The multi-energy system optimization method according to claim 1, characterized in that: The boundary constraints of the multi-energy system include: Energy storage system capacity constraints, grid constraints, photovoltaic equipment power constraints and charging pile power constraints.

3. The multi-energy system optimization method according to claim 2, characterized in that: The energy storage system capacity constraint is: in, For the The lower limit of the energy storage system, For the The initial capacity of the energy storage system, is the power change value at the end of the next time period and For the The upper limit of the energy storage system.

4. The multi-energy system optimization method according to claim 2, characterized in that: The grid constraints are: in: is the number of energy storage systems, is the initial energy storage cabinet, For the Energy storage cabinets in time period The power, is the maximum power limit allowed by the power grid, is the lower limit of the maximum power allowed by the power grid and is the load power currently measured by the power grid.

5. The multi-energy system optimization method according to claim 2, characterized in that: The photovoltaic equipment power constraint condition is: in, For the The power of each photovoltaic device in the current time period, For the The maximum power of each photovoltaic device and For the The minimum power of a photovoltaic device.

6. The multi-energy system optimization method according to claim 2, characterized in that: The charging pile power constraint condition is: in, For the The power of each charging pile in the current time period, For the The maximum power of each charging station and For the The minimum power of a charging station.

7. The multi-energy system optimization method according to claim 1, characterized in that: The optimal profit objective function is: in, To minimize the is the number of energy storage bins, is the number of time periods, The energy storage bin with index 0, is the time period with index 0, For the Energy storage system in time period The power, For the The electricity price for a period of time, is the length of the time period, is the number of PV systems, is the photovoltaic system with index 0, For the The power of a photovoltaic system in the current time period, is the initial electricity price, is the number of V2G or ordinary charging piles, For V2G or ordinary charging piles with index 0 and For the The power of a charging pile in the current time period.

8. A multi-energy system optimization device, characterized in that: include: An acquisition module, used to acquire multi-energy system data; The multi-energy system data are: grid load power, energy storage capacity, energy storage power, charging pile power and photovoltaic equipment power; Boundary constraint condition module, used to construct boundary constraint conditions of multi-energy system based on multi-energy system data; The fixed coefficient module is used to obtain the power grid price data, establish a discrete time model based on the power grid price data, and obtain the fixed coefficients of the multi-energy system based on the discrete time model; the fixed coefficients of the multi-energy system are: the time of the multi-energy system, the constants in the linear constraints and the coefficients of the decision variables; The optimal benefit objective function module is used to establish the optimal benefit objective function based on the fixed coefficients of the multi-energy system; The output module is used to solve the optimal benefit objective function based on the linear programming algorithm and the boundary constraints of the multi-energy system to obtain the optimization plan of the multi-energy system.

9. An electronic device, characterized in that: comprising a memory and a processor, wherein: The memory is used to store programs; The processor, coupled to the memory, is used to execute the program stored in the memory to implement the steps in the multi-energy system optimization method as described in any one of claims 1 to 7 above.

10. A computer-readable storage medium, characterized in that: Used to store computer-readable programs or instructions, which, when executed by a processor, can implement the steps in the multi-energy system optimization method described in any one of claims 1 to 7.

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