An energy system control method, an electronic device, a storage medium and an energy system

Abstract

The application relates to an energy system control method, an electronic device, a storage medium and an energy system, and belongs to the technical field of energy scheduling and control, wherein the method comprises the following steps: acquiring scheduling prediction data of an energy system, the system comprising an energy storage device and an energy supply device, the energy storage device comprising a virtual energy storage device; based on the scheduling prediction data, taking the energy storage and energy release power of the energy storage device and the energy generation power of the energy supply device as decision variables, taking the maximization of renewable energy consumption and the minimization of the total operation cost of the energy system as the target, and constructing a multi-objective optimization scheduling model; solving the multi-objective optimization scheduling model, determining an optimal device operation scheme, and controlling the energy storage device and the energy supply device based on the optimal device operation scheme. The application breaks the forced binding of cold and heat loads to local power generation loads by reusing existing sewage ponds, sludge digestion ponds and other facilities of a sewage plant, and realizes flexible decoupling.

Description

Technical Field

[0001] This application relates to the field of energy dispatch and control technology, and in particular to an energy system control method, electronic device, storage medium and energy system. Background Technology

[0002] To meet the continuous and stable cooling or heating load demands for sludge digestion, plant heating, and cooling, wastewater treatment plants are often equipped with combined cooling, heating, and power (CCHP) systems. These systems use natural gas as fuel to drive gas turbines to generate electricity, while simultaneously recovering waste heat generated during power generation for heating or cooling via absorption chillers, thereby achieving efficient energy utilization and reducing the overall energy consumption of the wastewater treatment plant.

[0003] Existing combined cooling, heating, and power (CCHP) systems in wastewater treatment plants typically operate under "cooling-driven power generation" or "heat-driven power generation" modes. The key to this mode is that the gas turbine must prioritize meeting the plant's cooling or heating load demands; power generation is merely a byproduct of fulfilling those demands. Consequently, the resulting power output is rigid: the generating capacity depends entirely on the real-time magnitude of the cooling or heating load and cannot be flexibly adjusted according to grid dispatch requirements.

[0004] The core flaw of the aforementioned "cooling-driven power generation" and "heat-driven power generation" operation modes lies in the rigid coupling relationship between heating or cooling demand and power generation output. When wind power output surges at night or during windy weather, the grid's absorption capacity is limited. Because the rigid power output of the gas turbines within the wastewater treatment plant cannot be reduced, their power generation continuously occupies a portion of the grid's absorption capacity, forcing the abandonment of clean and inexpensive wind power, resulting in insufficient wind energy absorption capacity in wastewater treatment plant scenarios. Summary of the Invention

[0005] In view of this, it is necessary to provide an energy system control method, electronic device, storage medium and energy system to solve the technical problem of insufficient wind energy absorption capacity in sewage treatment plant scenarios in the prior art.

[0006] To address the aforementioned problems, firstly, this application provides an energy system control method, comprising: Acquire scheduling and forecasting data for an energy system, the system including energy storage devices and energy supply devices connected to the energy storage devices, the energy storage devices including virtual energy storage devices with thermal inertia and temperature fluctuation range maintenance requirements; Based on the scheduling prediction data, with the energy storage and release power of the energy storage device and the production capacity power of the energy supply device as decision variables, and with the goal of maximizing renewable energy consumption and minimizing the total operating cost of the energy system, a multi-objective optimization scheduling model is constructed. The boundary values ​​of the energy storage and release characteristics constraints of the virtual energy storage device are dynamically determined based on its current state and a preset temperature fluctuation range. The multi-objective optimization scheduling model is solved to determine the optimal equipment operation scheme, and the energy storage equipment and the energy supply equipment are controlled based on the optimal equipment operation scheme.

[0007] In one implementation, the multi-objective optimization scheduling model is solved to determine the optimal equipment operation scheme, and the energy storage device and the energy supply device are controlled based on the optimal equipment operation scheme, including: Based on a preset multi-objective optimization algorithm, the multi-objective optimization scheduling model is solved to obtain a non-dominated solution set; The optimal equipment operation scheme is determined based on the non-dominated solution set, and the energy storage equipment and the energy supply equipment are controlled based on the optimal equipment operation scheme. The multi-objective optimization algorithm employs a particle swarm optimization algorithm that incorporates a neighborhood re-search mechanism.

[0008] In one implementation, the multi-objective optimization scheduling model is solved based on a preset multi-objective optimization algorithm to obtain a non-dominated solution set, including: S301. Initialize the particle population. Each particle in the population includes position and velocity. Each position represents a device operation plan. S302. Based on the position, velocity, individual optimal solution, and global optimal solution of each particle, update the velocity and position of each particle, calculate the objective function value pair of each particle, perform non-dominated sorting and crowding distance sorting based on the objective function value, and update the individual historical optimal solution of each particle and the global optimal position of all particles. The objective function value pair includes renewable energy consumption and total system operating cost. S303. Determine whether the number of iterations of the current particle swarm has reached the preset iteration stage switching threshold. If the number of iterations of the current particle swarm has not reached the preset iteration stage switching threshold, then execute S304. If the number of iterations of the current particle swarm has reached the preset iteration stage switching threshold, then calculate the spatial distance between each particle and the global optimal particle. When the spatial distance is less than the preset distance threshold, randomly update the particle's position in the neighborhood of its current position. When the distance is not less than the preset distance threshold, the particle retains its original position. S304. Determine whether the current number of iterations of the particle swarm has reached the preset maximum number of iterations. If the current number of iterations of the particle swarm has not reached the preset maximum number of iterations, return to step S302. If the current number of iterations of the particle swarm has reached the preset maximum number of iterations, terminate the iteration and output the non-dominated solution set.

[0009] In one embodiment, the power supply equipment includes an electric boiler.

[0010] In one embodiment, the total operating cost of the system includes the cost of purchasing electricity from the external power grid, the cost of purchasing natural gas from the natural gas supplier, and the depreciation cost of the physical energy storage equipment, wherein the cost of purchasing electricity is calculated based on a time-of-use electricity pricing model, and the cost of purchasing natural gas is calculated based on a tiered gas pricing model.

[0011] In one embodiment, the energy storage and dissipation characteristics of the virtual energy storage device are constrained as follows: when the virtual energy storage device is in a heat storage state, its heat storage power does not exceed the current maximum heat storage power; when the virtual energy storage device is in a heat dissipation state, its heat dissipation power does not exceed the current maximum heat dissipation power. The maximum thermal storage power is the smaller of the rated power of the heat exchange equipment configured in the virtual energy storage device and the current adjustable thermal storage power; the maximum heat release power is the smaller of the rated power of the heat exchange equipment and the current adjustable heat release power. The adjustable thermal storage power and the adjustable heat release power are determined based on the current medium temperature of the virtual energy storage device, the preset temperature fluctuation range, and the medium heat capacity.

[0012] In one embodiment, the energy storage and release characteristics constraints of the virtual energy storage device include: the temperature of the virtual energy storage device in the current time period is calculated recursively from the temperature of the previous time period and the net heat storage of the current time period, and the temperature of the virtual energy storage device at any time is within a preset temperature fluctuation range.

[0013] Secondly, this application also provides an electronic device, including a memory and a processor; 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 of the energy system control method described above.

[0014] Thirdly, this application also provides a computer-readable storage medium storing a program or instructions that, when executed by a processor, implement the steps of the energy system control method described above.

[0015] Fourthly, the present invention also provides an energy system, comprising: Energy storage device, including virtual energy storage device with thermal inertia and the need to maintain a temperature fluctuation range; An energy supply device, connected to the energy storage device, is used to supply energy to the energy storage device; The control center is communicatively connected to both the energy supply equipment and the energy storage equipment, and is used to execute the energy system control method described above.

[0016] The beneficial effects of this application are as follows: The energy system control method provided by this application requires no new civil engineering investment. It only reuses existing facilities such as sewage ponds and sludge digestion ponds in sewage treatment plants, and uses them as virtual energy storage devices. By utilizing their thermal inertia, excess or low-cost energy is converted into thermal energy for storage. The stored thermal energy can be directly used to meet heating or cooling needs without the need for real-time supply from gas turbines. This breaks the forced binding of heating and cooling loads to local power generation loads, achieving flexible decoupling. When wind power output surges at night or during windy weather, the heating or cooling needs that originally had to be met by gas turbines in real time can be met by releasing the stored thermal energy from the virtual energy storage device. The power generation output of the gas turbines can be flexibly reduced, freeing up the system absorption space that was originally occupied, allowing clean wind power to be absorbed by the system, reducing wind curtailment, and thus improving the wind energy absorption capacity in sewage treatment plant scenarios. Attached Figure Description

[0017] Figure 1 A schematic diagram of the energy system provided in the embodiments of this application; Figure 2 A flowchart illustrating the energy system control method provided in this application embodiment; Figure 3 A flowchart illustrating the multi-objective optimization algorithm provided in the embodiments of this application; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0018] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0019] It should be understood that the illustrative drawings are not drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of this application. It should be understood that the operations in the flowcharts may be implemented out of order, and steps without logical contextual relationships may be reversed or performed simultaneously. Furthermore, those skilled in the art, guided by the content of this application, may add one or more other operations to the flowcharts, or remove one or more operations from the flowcharts. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor systems and / or microcontroller systems.

[0020] The terms "first," "second," etc., used in the embodiments of this application are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a technical feature specified with "first" or "second" may explicitly or implicitly include at least one of those features. "And / or" describes the relationship between related objects, indicating that three relationships may exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone.

[0021] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0022] This application provides an energy system control method, electronic device, storage medium, and energy system, which are described below.

[0023] For ease of understanding, this application first describes the energy system, such as... Figure 1 As shown, an embodiment of the present invention provides an energy system 100, comprising: Energy storage device 101 includes virtual energy storage devices. Virtual energy storage devices refer to facilities with thermal inertia and the need to maintain operations within a temperature fluctuation range. They are not specifically designed for energy storage, but rather utilize digital means to extract equivalent energy storage resources from their inherent thermal inertia. Virtual energy storage devices include, but are not limited to, existing facilities in the factory area such as sewage treatment plants, sludge digestion tanks, and offices. No additional civil engineering investment is required; considerable flexible regulation capacity can be obtained simply by reusing the inherent heat capacity of the facility itself. They have the advantages of large energy storage capacity and low cost, but their response speed is relatively slow.

[0024] In some embodiments, the energy storage device 101 further includes a physical energy storage device, which generally refers to a facility that stores energy using a dedicated physical device or medium, including but not limited to batteries and thermal storage tanks. The thermal storage tank is used to achieve rapid storage and release of thermal energy, with fast response speed and high control precision, making it suitable for smoothing short-term heat load fluctuations.

[0025] The aforementioned virtual energy storage devices, together with physical energy storage devices, constitute a multi-element energy storage system. By unifying the adjustable power of each virtual energy storage device with the energy storage parameters of the physical energy storage devices through unified modeling, a multi-element energy storage system covering both electrical and thermal energy forms can be formed, possessing the advantages of rapid response, large capacity, and low cost, thus providing abundant regulation resources for subsequent optimized scheduling.

[0026] It should also be noted that the method of this application can be applied to any system that contains a virtual energy storage device with thermal inertia and a need to maintain a temperature fluctuation range. In this embodiment, the application scenario is a combined cooling, heating, and power system in a sewage treatment plant. In other embodiments, the application scenarios can also be industrial parks, aquaculture parks, data center parks, etc.

[0027] The power supply device 102 is connected to the energy storage device 101 and is used to supply power to the energy storage device 101.

[0028] In some embodiments, the energy supply equipment 102 includes heating equipment, cooling equipment, and local renewable energy power generation equipment. The heating equipment includes at least one of a gas turbine and an electric boiler; the cooling equipment includes at least one of an absorption chiller and an electric chiller; and the renewable energy source includes at least one of wind power and solar power. The electric boiler can convert excess electricity generated by renewable energy sources into heat energy when there is a surplus, and store it in the energy storage equipment 101, further improving the utilization of renewable energy.

[0029] The control center 103 is communicatively connected to the energy supply equipment 102 and the energy storage equipment 101, respectively, and is used to execute energy system control methods to control the energy supply equipment 102 and the energy storage equipment 101 to absorb as much renewable energy as possible.

[0030] The energy system 100 described above can be used to implement the energy system control method described in the embodiments of this application. The specific implementation principle of each unit can be found in the corresponding content in the subsequent method embodiments, and will not be repeated here.

[0031] Based on the above energy system, Figure 2 A flowchart illustrating the energy system control method provided in this application embodiment is shown below. Figure 2 As shown, energy system control methods include: S201. Obtain scheduling and forecasting data for the energy system; The dispatch forecast data includes energy load forecast data within a preset dispatch cycle, local renewable energy forecast power generation, and external energy price data. Among them, the energy load forecast data includes cooling load forecast data, heating load forecast data, and electricity load forecast data. The external energy price data includes the purchase price of electricity from the grid (determined based on the time-of-use pricing model), the electricity sales price to the grid (determined based on the time-of-use pricing model), and the purchase price of gas from the gas company (determined based on the tiered gas pricing model).

[0032] S202. Based on scheduling and prediction data, with the energy storage and release power of energy storage devices and the production capacity power of energy supply devices as decision variables, and with the goal of maximizing renewable energy consumption and minimizing the total operating cost of the energy system, a multi-objective optimization scheduling model is constructed. The boundary values ​​of the energy storage and release characteristics constraints of virtual energy storage devices are dynamically determined based on their current state and the preset temperature fluctuation range. In this embodiment, the decision variables are first defined as follows: the energy storage and release power of the energy storage device specifically includes the heat storage power and heat release power of the virtual energy storage device, the heat storage power and heat release power of the heat storage tank, and the charging power and discharging power of the battery; the production power of the energy supply device specifically includes the power generation power of the gas turbine, the power consumption power of the electric boiler, the heat consumption power of the absorption chiller, and the power consumption power of the electric chiller; in addition, the decision variables may also include the power purchased from the grid and the power sold to the grid.

[0033] Reconstruct the objective function: To maximize renewable energy consumption: In order to utilize as much local renewable energy generation as possible, as well as green electricity purchased from the grid (prioritizing the consumption of local renewable energy, with surplus electricity sold back to the grid), reduce wind and solar curtailment, and increase the proportion of renewable energy, this application takes maximizing renewable energy consumption as the optimization objective; specifically, renewable energy consumption includes local renewable energy consumption and renewable energy consumption in electricity purchased from the grid, and the formula for maximizing renewable energy consumption is: ; ; ; In the formula, This represents the total amount of renewable energy consumed during the dispatch cycle. Indicates the scheduling period number. , Indicates the total number of time periods in the scheduling cycle. Indicates the duration of a scheduling period. Indicates the first Actual local renewable energy consumption power during the time period (derived variable). Indicates the first Power purchased from the grid during specific time periods Indicates the first The proportion of renewable energy in the power grid during the time period (a derived variable, determined by the local renewable energy forecast power obtained from S201). Indicates the first Forecasted local renewable energy generation capacity for the time period (obtained from S201). Indicates the first Total power consumption of the system during the time period (derived variable). Indicates the first Forecasted electrical load for the time period (obtained from S201). Indicates the first Electricity consumption of the boiler during a given time period (decision variable). Indicates the first Battery charging power over time period (decision variable). Indicates the first Power consumption of the chiller during the time period (decision variable). Indicates the first Electricity sold to the grid during different time periods (decision variable) Indicates the first Gas turbine power generation during a given time period (decision variable). Indicates the first Battery discharge power over a given period (decision variable).

[0034] Considering It is not necessary to accurately reflect the proportion of green electricity in the power grid, but only to reflect the trend, in order to avoid dependence on external data. The value can be set based on local renewable energy forecast data: when the local renewable energy forecast power generation is high, it indicates that the wind and solar resources in the region are generally good during that period, and the proportion of green electricity in the grid is also high. Set a larger value, and vice versa. Specifically, The value is set between 0 and 1 and is proportional to the predicted power of local renewable energy.

[0035] To minimize the total system operating cost: In order to reduce electricity purchase costs, gas purchase costs, and energy storage depreciation costs, and improve economic efficiency, this application takes minimizing the total system operating cost as the optimization objective. Specifically, the total system operating cost includes the cost of purchasing electricity from the grid, the cost of selling electricity to the grid, the cost of purchasing gas from the gas company, and the depreciation cost of physical energy storage equipment. The electricity purchase cost is calculated based on a time-of-use pricing model, and the gas purchase cost is calculated based on a tiered gas pricing model. The formula corresponding to minimizing the total system operating cost is: ; In the formula, This represents the total system operating cost within the scheduling period. Indicates the first Time-of-use electricity price for grid purchases (obtained from S201). Indicates the first Power purchased from the grid during a given time period (decision variable). Indicates the first Electricity price during specific time periods (obtained from S201). Indicates the first Electricity sold to the grid during a given time period (decision variable) Indicates the first The time-limited gas purchase price (its value is determined jointly based on the cumulative gas consumption up to the time period and the tiered gas price model obtained from S201). Indicates the first The gas consumption rate of the gas turbine during a given period (a derived variable; the gas consumption rate of the gas turbine is directly proportional to its power generation, with the proportionality coefficient being the power consumption rate). This represents the depreciation cost of physical energy storage equipment (based on the investment cost and usage frequency of batteries and thermal storage tanks, converted to the dispatch cycle).

[0036] Finally, the constraints are constructed, including electrical, thermal, and cooling power balance constraints and equipment operation constraints.

[0037] Specifically, regarding the power balance constraints for electricity, heat, and cooling, to ensure a complete balance between power generation, power purchase, power sale, energy storage charging and discharging, and electrical load within the system at each time period, and to meet electricity demand, this embodiment sets an electrical power balance constraint. Specifically, the electrical power balance constraint formula is: ; In the formula, Indicates the first Power generation of the gas turbine during a given time period (decision variable). Indicates the first Discharge power of the battery during a given time period (decision variable). Indicates the first Power purchased from the grid during a given time period (decision variable). Indicates the first Actual output of local renewable energy during the time period (derived variable). Indicates the first Forecasted electrical load for the time period (obtained from S201). Indicates the first Power consumption of electric boilers during specific time periods (decision variable). Indicates the first The charging power of the battery during a given time period (decision variable). Indicates the first Power consumption of the time-limited electric chiller (decision variable). Indicates the first Electricity sold to the grid during a given time period (decision variable).

[0038] To ensure a balance between system heating, heat storage and release, and heat load in each time period, this embodiment sets a heat power balance constraint. Specifically, the heat power balance constraint formula is: ; In the formula, Indicates the first The heat production power of the gas turbine during the time period (a derived variable; the heat production power of the gas turbine is directly proportional to its power generation power, and the proportionality coefficient is the heat-to-power ratio of the gas turbine). Indicates the first The heating power of the electric boiler during a given period (a derived variable; the heating power of the electric boiler is determined by its power consumption, and the two are linearly positively correlated; the proportionality coefficient is the electrothermal conversion efficiency of the electric boiler). Indicates the first Heat release power of the time-period thermal storage tank (decision variable). Indicates the first Heat release power of virtual energy storage devices during a given time period (decision variable). Indicates the first Forecasted heat load for the period (obtained from S201). Indicates the first Thermal storage capacity of time-segmented thermal storage tanks (decision variable). Indicates the first Thermal storage capacity of virtual energy storage devices during different time periods (decision variable). Indicates the first Heat consumption power of time-period absorption chiller (decision variable).

[0039] To ensure a balance between cooling output and cooling load at each time period, this embodiment sets a cooling power balance constraint. Specifically, the cooling power balance constraint formula is as follows: ; In the formula, Indicates the first The cooling power of a time-period absorption chiller (a derived variable; the cooling power of an absorption chiller is directly proportional to its heat consumption power, and the proportionality coefficient is the thermodynamic coefficient of the absorption chiller). Indicates the first The cooling power of the electric chiller during the time period (a derived variable; the cooling power of the electric chiller is directly proportional to its power consumption, and the proportionality coefficient is the energy efficiency ratio of the electric chiller). Indicates the first Forecast value of cooling load for each period (obtained from S201).

[0040] Batteries cannot be charged and discharged simultaneously within the same dispatch period; thermal storage tanks cannot store and release heat simultaneously within the same dispatch period; energy systems cannot purchase and sell electricity to the grid simultaneously within the same dispatch period.

[0041] To ensure safe operation, the output of power supply equipment must be limited to its physical limits; the charging and discharging power and capacity of batteries in physical energy storage devices must be limited, and the heat storage tank must have its heat storage and release power and heat storage capacity limited. These are all standard constraints and will not be elaborated further here.

[0042] The energy storage and release characteristics constraints of virtual energy storage devices include: when the virtual energy storage device is in thermal storage mode, its thermal storage power shall not exceed its maximum thermal storage power; when the virtual energy storage device is in thermal release mode, its thermal release power shall not exceed its maximum thermal release power; the maximum thermal storage power is the smaller of the rated power of the heat exchange equipment configured in the virtual energy storage device and the current adjustable thermal storage power; the maximum thermal release power is the smaller of the rated power of the heat exchange equipment and the current adjustable thermal release power; the adjustable thermal storage power and adjustable thermal release power are determined by the current medium temperature, the preset temperature fluctuation range, and the medium heat capacity of the virtual energy storage device, and change dynamically with the current operating state of the virtual energy storage device; the current temperature of the virtual energy storage device is calculated recursively from the temperature of the previous moment and the net heat storage of the current moment (net heat storage = thermal storage power - thermal release power); the temperature of the virtual energy storage device at any time must be maintained within the preset temperature fluctuation range.

[0043] In some embodiments, considering that the virtual energy storage device, due to its internal containment of a large amount of high specific heat capacity medium (such as sewage, sludge, etc.), is essentially a thermomass with a huge heat capacity, and its heat storage and release processes follow the basic laws of thermodynamics, by real-time monitoring of the temperature of the virtual energy storage device and obtaining its characteristic parameters, including the temperature fluctuation range allowed by user comfort or process requirements (such as ±2℃), the effective volume of the tank, the density and specific heat capacity of the medium, and the rated power of the configured heat exchange equipment, a temperature-heat conversion relationship model can be established, thereby quantifying its dispatchability. Specifically, the energy storage and release characteristic constraint formulas of the virtual energy storage device are as follows: ; ; ; ; ; ; In the formula, Indicates the first Thermal storage capacity of virtual energy storage devices during specific time periods; Indicates the first The heat dissipation power of virtual energy storage devices during a given time period; Indicates the first The heat storage / release state variable of the virtual energy storage device during a given time period: a value of 1 indicates that the virtual energy storage device is in heat storage state, and a value of 0 indicates that the virtual energy storage device is in heat release state. Indicates the first The maximum thermal storage capacity of the virtual energy storage device during the time period Indicates the first The maximum heat release power of the virtual energy storage device during a given time period is dynamically calculated based on the current temperature conditions. This indicates the rated power of the heat exchange equipment (such as heat pumps, heat exchangers, etc.) connected to the corresponding virtual energy storage device. Indicates the first The medium temperature of the virtual energy storage device during the time period. Indicates the first The medium temperature of the virtual energy storage device during the time period. The medium heat capacity of the virtual energy storage device is represented by the product of the effective volume of the virtual energy storage device, the medium density, and the specific heat capacity of the medium. This indicates the lower limit of the dielectric temperature of the virtual energy storage device allowed by the process. This indicates the upper limit of the medium temperature allowed by the process for virtual energy storage devices.

[0044] It should be noted that this embodiment assumes no energy loss during the heat exchange process. In practical applications, the maximum heat storage and heat release power of the virtual energy storage device can be calculated based on the efficiency of the heat exchange equipment.

[0045] S203. Solve the multi-objective optimization scheduling model to determine the optimal equipment operation scheme, and control the energy storage equipment and energy supply equipment based on the optimal equipment operation scheme.

[0046] In some embodiments, step S203 includes: solving the multi-objective optimization scheduling model based on a preset multi-objective optimization algorithm to obtain a non-dominated solution set; determining the optimal equipment operation scheme based on the non-dominated solution set; and controlling the energy storage device and the energy supply device based on the optimal equipment operation scheme. In other embodiments, a weighted approach may also be used to solve the model.

[0047] Considering that the virtual energy storage constraints (adjustable capacity and power limitations) in the combined cooling, heating and power system result in multiple feasible regions in the solution space, the traditional particle swarm optimization algorithm is prone to getting trapped in local optima. Therefore, the multi-objective optimization algorithm adopts a particle swarm optimization algorithm that introduces a neighborhood re-search mechanism. During the iteration process, particles trapped in local optima are randomly reset in their neighborhoods to enhance the global optimization capability.

[0048] Specifically, such as Figure 3 As shown, based on a pre-defined multi-objective optimization algorithm, the multi-objective optimization scheduling model is solved to obtain a non-dominated solution set, including: S301. Initialize the particle population. Each particle in the population includes position and velocity. Each position represents a device operation plan. Based on the configuration of each device in the energy system and the preset scheduling cycle, a population containing multiple particles is constructed. The position of each particle represents a complete device operation scheme, that is, the output or state sequence of each controllable device in all time periods within the scheduling cycle. The initial position of each particle is randomly generated within the allowed solution space and assigned a random initial velocity.

[0049] S302. Based on the position, velocity, individual optimal solution and global optimal solution of each particle, update the velocity and position of each particle, calculate the objective function value pair of each particle, perform non-dominated sorting and crowding distance sorting based on the objective function value, update the individual historical optimal solution of each particle and the global optimal position of all particles. The objective function value pair includes renewable energy consumption and total system operating cost. It should be noted that for each updated particle, its objective function value pair is calculated, which includes the renewable energy consumption and the total system operating cost. If the equipment operation plan corresponding to a particle violates the constraints, a penalty value is applied to its objective function value.

[0050] Perform Pareto non-dominated sorting on all particles in the current population and calculate the crowding distance. For each particle, compare its current position with its historical best position: if the current position dominates the historical best position, update the historical best position with the current position; if they do not dominate each other, randomly select one, or select the one with the larger crowding distance, as the new historical best position. After updating the historical best position, select the position of the particle with the largest crowding distance from the first non-dominated layer of the current population as the global best position.

[0051] S303. Determine whether the current iteration count of the particle swarm has reached the preset iteration stage switching threshold. If the current iteration count of the particle swarm has not reached the preset iteration stage switching threshold, then execute S304. If the current iteration count of the particle swarm has reached the preset iteration stage switching threshold, then calculate the spatial distance between each particle and the global optimal particle. When the spatial distance is less than the preset distance threshold, randomly update the particle's position in the neighborhood of its current position. When the distance is not less than the preset distance threshold, the particle retains its original position. It should be noted that in the above process, in the early stage of iteration, the standard particle swarm strategy is used for global search, so that the population moves quickly toward the Pareto front region; in the middle and late stages of iteration, the neighborhood re-search mechanism is used to make the particles trapped in local optima jump out of the current region and continue to explore a better solution space.

[0052] S304. Determine whether the current number of iterations of the particle swarm has reached the preset maximum number of iterations. If the current number of iterations of the particle swarm has not reached the preset maximum number of iterations, return to step S302. If the current number of iterations of the particle swarm has reached the preset maximum number of iterations, terminate the iteration and output the non-dominated solution set.

[0053] Decision-makers can select an optimal compromise solution from the non-dominated solution set based on their actual preferences, and after decoding, obtain the optimal equipment operation plan for each device within the scheduling cycle.

[0054] Through this dual mechanism of "global search + local escape," the algorithm can output a set of non-dominated solutions after iteration. Decision-makers can select an optimal compromise solution from these solutions based on their actual preferences (such as prioritizing wind energy consumption or economic efficiency), and then decode the solution to form a detailed equipment operation plan.

[0055] The equipment operation plan specifically outlines the heat storage and release power of the virtual energy storage equipment, the heat storage and release power of the heat storage tank, the charging and discharging power of the battery, the power generation power of the gas turbine, the power consumption of the electric boiler, the heat consumption of the absorption chiller, and the power consumption of the electric chiller during the future scheduling cycle, so as to carry out dynamic coordination and control of the subsequent energy storage equipment and energy supply equipment.

[0056] Ultimately, the goal is to prioritize the consumption of wind power when its predicted output is high, and to utilize a multi-energy storage system to absorb excess electricity or heat. Specifically: when wind power is surplus and electricity prices are low or gas prices are high, electric boilers will be put into operation first to generate heat using the surplus wind power, and the heat will be stored in thermal storage tanks or virtual energy storage units consisting of sewage tanks and sludge digestion tanks; when wind power is insufficient and electricity prices are high, thermal storage tanks and virtual energy storage units will be used first to release the stored heat, reducing the power generation and heat production of gas turbines, thereby reducing the cost of purchasing electricity and gas; during the summer when there is a large peak-valley difference in electricity prices, the control strategy aims to reduce the purchase of electricity from the grid during peak hours, using a multi-energy storage system to store energy during off-peak hours and release energy during peak hours; during the winter when electricity prices are relatively stable, the control strategy aims to maintain stable power on the interconnection line with the external power grid, using a multi-energy storage system to smooth out wind power fluctuations and load changes.

[0057] Compared with existing technologies, this application requires no new civil engineering investment. It only reuses existing facilities in wastewater treatment plants, such as sewage ponds and sludge digestion ponds, as virtual energy storage devices. Utilizing their thermal inertia, excess or low-cost energy is converted into stored thermal energy. The stored thermal energy can be directly used to meet heating or cooling needs without requiring real-time supply from gas turbines. This breaks the forced binding of heating and cooling loads to local power generation loads, achieving flexible decoupling. When wind power output surges at night or during windy weather, heating or cooling demands that would otherwise have to be met by gas turbines can be satisfied by releasing the stored thermal energy from the virtual energy storage device. The gas turbine's power generation output can be flexibly reduced, freeing up previously occupied system absorption space. This allows clean wind power to be absorbed by the system, reducing wind curtailment and improving wind energy absorption capacity in wastewater treatment plant scenarios.

[0058] like Figure 4 As shown, this application also provides an electronic device, which can be an industrial control computer, an embedded controller, or a PLC programmable logic controller, and the electronic device includes at least a processor 401 and a memory 402.

[0059] The processor 401 may include one or more processing cores, implemented using at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), or PLA (Programmable Logic Array), to meet the processing requirements for real-time control and data acquisition in industrial environments. In some embodiments, the processor 401 may also employ an embedded processor with an ARM architecture or similar.

[0060] The memory 402 may include one or more non-transitory computer-readable storage media, as well as high-speed random access memory and non-volatile memory. The non-transitory computer-readable storage media stores at least one instruction, which, when executed by the processor 401, implements the energy system control method provided in the embodiments of this application, including the acquisition of scheduling prediction data, the construction and solution of a multi-objective optimization scheduling model, and the generation and distribution of equipment operation plans.

[0061] In some embodiments, the electronic device further includes a peripheral device interface and at least one peripheral device. The processor 401, memory 402, and peripheral device interface are connected via a bus or signal line. The peripheral device is connected to the peripheral device interface via a bus, signal line, or circuit board. Indicatively, the peripheral device includes: a communication interface, a data acquisition interface, a display device, and a power supply.

[0062] The communication interface is used to interact with the energy supply equipment, energy storage equipment and external energy management system in the energy system, including at least one of Ethernet interface, RS485 interface and CAN bus interface; the data acquisition interface is used to acquire the operating status data and energy load data of each device in the energy system, including analog input interface and digital input interface; the display device is used to display the equipment operation plan and system operating status; the power supply is used to supply power to the electronic equipment.

[0063] Accordingly, this application also provides a computer-readable storage medium for storing computer-readable programs or instructions. When the programs or instructions are executed by a processor, they can implement the steps or functions of the energy system control methods provided in the above-described method embodiments.

[0064] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0065] The above provides a detailed description of an energy system control method provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

[0066] The above description is merely a preferred embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. An energy system control method, characterized in that, include: Acquire scheduling and forecasting data for an energy system, the system including energy storage devices and energy supply devices connected to the energy storage devices, the energy storage devices including virtual energy storage devices with thermal inertia and temperature fluctuation range maintenance requirements; Based on the scheduling prediction data, with the energy storage and release power of the energy storage device and the production capacity power of the energy supply device as decision variables, and with the goal of maximizing renewable energy consumption and minimizing the total operating cost of the energy system, a multi-objective optimization scheduling model is constructed. The boundary values ​​of the energy storage and release characteristics constraints of the virtual energy storage device are dynamically determined based on its current state and a preset temperature fluctuation range. The multi-objective optimization scheduling model is solved to determine the optimal equipment operation scheme, and the energy storage equipment and the energy supply equipment are controlled based on the optimal equipment operation scheme.

2. The energy system control method according to claim 1, characterized in that, Solving the multi-objective optimization scheduling model to determine the optimal equipment operation scheme, and controlling the energy storage equipment and the energy supply equipment based on the optimal equipment operation scheme, including: Based on a preset multi-objective optimization algorithm, the multi-objective optimization scheduling model is solved to obtain a non-dominated solution set; The optimal equipment operation scheme is determined based on the non-dominated solution set, and the energy storage equipment and the energy supply equipment are controlled based on the optimal equipment operation scheme. The multi-objective optimization algorithm employs a particle swarm optimization algorithm that incorporates a neighborhood re-search mechanism.

3. The energy system control method according to claim 2, characterized in that, Based on a preset multi-objective optimization algorithm, the multi-objective optimization scheduling model is solved to obtain a non-dominated solution set, including: S301. Initialize the particle population. Each particle in the population includes position and velocity. Each position represents a device operation plan. S302. Based on the position, velocity, individual optimal solution, and global optimal solution of each particle, update the velocity and position of each particle, calculate the objective function value pair of each particle, perform non-dominated sorting and crowding distance sorting based on the objective function value, and update the individual historical optimal solution of each particle and the global optimal position of all particles. The objective function value pair includes renewable energy consumption and total system operating cost. S303. Determine whether the number of iterations of the current particle swarm has reached the preset iteration stage switching threshold. If the number of iterations of the current particle swarm has not reached the preset iteration stage switching threshold, then execute S304. If the number of iterations of the current particle swarm has reached the preset iteration stage switching threshold, then calculate the spatial distance between each particle and the global optimal particle. When the spatial distance is less than the preset distance threshold, randomly update the particle's position in the neighborhood of its current position. When the distance is not less than the preset distance threshold, the particle retains its original position. S304. Determine whether the current number of iterations of the particle swarm has reached the preset maximum number of iterations. If the current number of iterations of the particle swarm has not reached the preset maximum number of iterations, return to step S302. If the current number of iterations of the particle swarm has reached the preset maximum number of iterations, terminate the iteration and output the non-dominated solution set.

4. The energy system control method according to claim 1, characterized in that, The power supply equipment includes an electric boiler.

5. The energy system control method according to claim 1, characterized in that, The total operating cost of the system includes the cost of purchasing electricity from the external power grid, the cost of purchasing natural gas from the natural gas supplier, and the depreciation cost of the physical energy storage equipment. The cost of purchasing electricity is calculated based on a time-of-use electricity pricing model, and the cost of purchasing natural gas is calculated based on a tiered gas pricing model.

6. The energy system control method according to claim 1, characterized in that, The energy storage and release characteristics of the virtual energy storage device are constrained as follows: when the virtual energy storage device is in a heat storage state, its heat storage power does not exceed the current maximum heat storage power; when the virtual energy storage device is in a heat release state, its heat release power does not exceed the current maximum heat release power. The maximum thermal storage power is the smaller of the rated power of the heat exchange equipment configured in the virtual energy storage device and the current adjustable thermal storage power; the maximum heat release power is the smaller of the rated power of the heat exchange equipment and the current adjustable heat release power. The adjustable thermal storage power and the adjustable heat release power are determined based on the current medium temperature of the virtual energy storage device, the preset temperature fluctuation range, and the medium heat capacity.

7. The energy system control method according to claim 1, characterized in that, The constraints on the energy storage and release characteristics of the virtual energy storage device include: the temperature of the virtual energy storage device at the current time period is calculated recursively from the temperature at the previous time period and the net heat storage of the current time period; and the temperature of the virtual energy storage device at any time is within a preset temperature fluctuation range.

8. An electronic device, characterized in that, Including memory and processor; 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 of the energy system control method according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the steps of the energy system control method according to any one of claims 1 to 7.

10. An energy system, characterized in that, include: Energy storage device, including virtual energy storage device with thermal inertia and the need to maintain a temperature fluctuation range; An energy supply device, connected to the energy storage device, is used to supply energy to the energy storage device; The control center is communicatively connected to both the energy supply equipment and the energy storage equipment, and is used to execute the energy system control method as described in any one of claims 1 to 7.

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