A method and device for collaborative optimization operation of a fishery comprehensive energy system

Abstract

The application relates to a fishery comprehensive energy system collaborative optimization operation method and device, and belongs to the technical field of comprehensive energy system scheduling, which comprises the following steps: respectively establishing a biomass combined heat and power model, a battery simulation model, a heat storage tank simulation model and an electric heating model according to biomass combined heat and power, a battery, a heat storage tank and electric heating contained in the comprehensive energy system; aiming at the uncertainty of the source and load sides, adopting a fuzzy chance constraint method to relax the deterministic constraint into a system constraint containing a fuzzy variable, and utilizing trapezoidal fuzzy parameters to clarify the system constraint; establishing a comprehensive demand response model; establishing a master-slave game double-layer optimization model of the comprehensive energy system; and adopting a particle swarm algorithm combined with an integer linear programming method to solve the master-slave game double-layer optimization model, so that the benefits of the energy supply side and the user side can be effectively balanced, the demand response potential of the user can be fully developed, and the economic and flexible collaborative optimization operation of the fishery comprehensive energy system can be realized.

Description

Technical Field

[0001] This application relates to the field of integrated energy system scheduling technology, and in particular to a method and apparatus for the coordinated optimization of the operation of an integrated energy system in a fishing ground. Background Technology

[0002] With the development and reform of integrated energy systems and electricity markets, the coupling relationship between energy sources and loads has become increasingly apparent. Electricity prices not only affect load demand, but loads also have a reciprocal effect on electricity prices. Therefore, game theory is increasingly being applied to areas such as the optimal operation of energy systems to address conflicts of interest among multiple stakeholders and make rational decisions.

[0003] Current research primarily utilizes scenario analysis, stochastic programming, and robust optimization methods to address uncertainties related to renewable energy and load in integrated energy systems. Demand response, by guiding users to change their energy consumption patterns, achieves peak shaving and valley filling, thereby reducing the operating costs of integrated energy systems. While much literature has researched integrated demand response for integrated energy systems, most studies neglect the issue of decreased comfort due to user-involved demand response. Therefore, establishing a demand response mechanism that considers various environmental disturbances and reflects actual user energy consumption remains a pressing technical challenge. Summary of the Invention

[0004] This application provides a method and device for the coordinated optimization of the operation of a fish farm integrated energy system, which can reasonably balance the interests of both the energy supply side and the energy consumption side, and is more in line with practical applications.

[0005] The technical solution of this application is as follows:

[0006] According to a first aspect of the embodiments of this application, a method for the coordinated optimization operation of a fishery integrated energy system is provided, comprising:

[0007] Based on the biomass cogeneration, batteries, thermal storage tanks, and electric heating included in the integrated energy system, respectively establish biomass cogeneration model, battery simulation model, thermal storage tank simulation model, and electric heating simulation model;

[0008] To address the uncertainties on both sides of the source load, a fuzzy chance constraint method is used to relax the deterministic constraints into system constraints with fuzzy variables, and trapezoidal fuzzy parameters are used to clarify the system constraints.

[0009] Establish a comprehensive demand response model, which includes an electricity demand response model and a heat demand response model;

[0010] A master-slave game-themed two-layer optimization model for an integrated energy system is established, which includes an operator layer and a user layer.

[0011] The particle swarm optimization algorithm combined with integer linear programming is used to solve the master-slave game two-level optimization model. On the operator side, the purchase and sale price of electricity and heat is set according to the market supply and demand relationship, and the objective function is to maximize the revenue within a day; on the user side, the optimal output and load demand are determined according to the operator's price signal.

[0012] Optionally, based on the biomass cogeneration, battery, thermal storage tank, and electric heating components included in the integrated energy system, a biomass cogeneration model, a battery simulation model, a thermal storage tank simulation model, and an electric heating simulation model are established respectively:

[0013] The biomass cogeneration model for integrated energy systems is as follows:

[0014]

[0015]

[0016] in, The electrical power of CHP during time period t. Let α be the thermal power of CHP during time period t. e The electrical conversion efficiency (%) of the CHP system, α h For the thermal conversion efficiency of the CHP system, η represents the biomass burned during time period t. b For biomass combustion conversion efficiency, f NCVb Δt represents the net calorific value of the biomass used, and Δt represents the scheduling time.

[0017] A battery simulation model is established for the batteries included in the integrated energy system as follows:

[0018]

[0019] in, It is the SOC value of the battery. satisfy and These are the charging and discharging power of the battery, respectively. satisfy

[0020] satisfy Δt is the time interval. and These are the charging and discharging status marker bits for the battery, respectively.

[0021] A simulation model of the thermal storage tank included in the integrated energy system is established as follows:

[0022]

[0023] in, For storing thermal energy in the heat storage tank, satisfy and These represent the heat storage and heat release power of the heat storage tank, respectively. satisfy satisfy η tst,chr The energy loss rate of the thermal storage tank. and These are the charging and discharging markers for the thermal storage tank. and satisfy

[0024] The following simulation model for electric heating is established for the electric heating included in the integrated energy system:

[0025]

[0026] In the formula, Let η be the heat output and power consumption of the electric heating equipment at time t. rl For electric heating efficiency.

[0027] Optionally, in response to the uncertainties on both sides of the source load, a fuzzy chance constraint method is used to relax the deterministic constraints into system constraints containing fuzzy variables, and trapezoidal fuzzy parameters are used to clarify the system constraints.

[0028] To address the uncertainties on both the source and load sides, a fuzzy chance constraint method is used to introduce fuzzy parameters for wind power. Photovoltaic fuzzy parameters Load fuzzy parameters

[0029] After fuzzy chance constraint processing, when the confidence level α ≥ 1 / 2, the system chance constraint P is applied using trapezoidal fuzzy parameters. r The clear equivalence class {g(x,ξ)≤0}≥α represents the system clearing constraint as follows:

[0030]

[0031] in, Let's assume two functions, where h0(x) is a part of the function, and θ... k1 ~θ k3 Let be the membership parameter, k = 1, 2, ..., t, t ∈ R.

[0032] Optionally, fuzzy parameters for wind power can be introduced using a fuzzy chance constraint method. Photovoltaic fuzzy parameters Load fuzzy parameters The triangular fuzzy parameters used in the process are: in:

[0033] P1=μ1P f,t, P2=μ2P f,t P3=μ3P f,t , This is a fuzzy expression for wind power, solar power output, or load forecasting, where P1~P3 are the corresponding triangular membership parameters, μ1~μ3 are proportionality coefficients, and P... f,t Let t be the predicted value of photovoltaic, wind power, or load at time t.

[0034] Optionally, using trapezoidal fuzzy parameters to clarify system constraints also includes:

[0035] Convert the electrical and thermal power balance constraint into:

[0036]

[0037]

[0038] in, These are, respectively, at time t: photovoltaic power output, wind power output, biomass cogeneration power generation, energy storage discharge, energy storage charging, purchasing electricity from the grid, selling electricity to the grid, and electricity consumption for heating. These represent the heat generation, heat storage absorption, heat storage release, heat load, and heat generation from electric heating at time t, respectively.

[0039] Optionally, establishing the comprehensive demand response model includes:

[0040] The electricity demand response model is established as follows:

[0041]

[0042]

[0043]

[0044] in, These are the user-side fixed electrical load, flexible electrical load, and additional electrical load consumed due to electric heating. This represents the electrical load adjustment at time t. This represents the adjusted flexible electrical load, where ε is the maximum allowable proportion of the electrical load adjustment at time t within a day, and k is the proportion of the total electrical load adjustment within a day.

[0045] According to the balance equation of heat demand and temperature and constraint relationships Establish a heat demand response model in, To maintain a constant heat load, The load is a flexible heat load, where S is the heating area of ​​the fishing ground, C is the heat capacity per unit heating area, and ω is the heat dissipation coefficient due to the temperature difference between the inside and outside. The water temperature and These are the lowest and highest room temperatures that fish can tolerate, respectively.

[0046] Optionally, the establishment of the master-slave game-theoretic two-level optimization model for the integrated energy system includes:

[0047] Based on user-side load demand and market supply and demand, a pricing strategy is formulated, and an operator-level optimization model is established with maximizing revenue as the objective function:

[0048]

[0049] in, These are, respectively, the electricity transactions between microgrid operators and users, and the revenue generated from providing heating to users; C grid C refers to the costs incurred from transactions with the power grid. om For the comprehensive energy system operation and maintenance costs, C cchp For fuel costs, of which, and satisfy Let be the electrical and thermal load power on the energy-consuming side at time t, respectively. These are the prices of electricity and heat sold to the energy-consuming side at time t, respectively. and The on-grid and retail electricity prices at time t are respectively, satisfying... Integrated energy system operation and maintenance costs adopt Calculate K i The operation and maintenance coefficients for each piece of equipment. The output power of each device;

[0050] Fuel cost C cchp use calculate, a represents the output electrical power of biomass cogeneration. e b e c e These represent the fuel cost coefficients for biomass cogeneration;

[0051] Using the difference between the user's utility function, energy cost, and fish temperature comfort penalty as the objective function, a user-level optimization model is established as follows:

[0052] max C user =C sa -C energy -C pu

[0053] in, C sa v represents the user's utility function. e α e v h α h This represents the preference coefficient for consuming electricity and heat. The prices at which operators sell electricity and heat to users; As a comfort penalty item, T represents the water temperature. in,set To set the optimal temperature in the water, ω represents the penalty factor.

[0054] Optionally, the step of using particle swarm optimization combined with integer linear programming to solve the master-slave game two-level optimization model includes:

[0055] The master-slave game model is solved using the particle swarm optimization algorithm and mixed-integer linear programming. The master-slave game model of the integrated energy system is solved by calling the Cplex solver through the Yalmip toolbox. On the operator side, the purchase and sale price of electricity and heat is set according to the market supply and demand relationship, and the objective function is to maximize the revenue within a day. On the user side, the optimal output and load demand are determined according to the price signals from the operator.

[0056] According to a second aspect of the embodiments of this application, a collaborative optimization operation device for a fishery integrated energy system is provided, comprising:

[0057] The model building module is used to build biomass cogeneration models, battery simulation models, and thermal storage tank simulation models based on the biomass cogeneration, batteries, and thermal storage tanks included in the integrated energy system.

[0058] The determination module is used to relax deterministic constraints into system constraints with fuzzy variables by using the fuzzy chance constraint method to address uncertainties on both sides of the source load, and to clarify the system constraints by using trapezoidal fuzzy parameters.

[0059] The processing module is used to establish a comprehensive demand response model, which includes an electrical demand response model and a thermal demand response model.

[0060] The optimization module is used to establish a master-slave game two-layer optimization model for the integrated energy system. The master-slave game two-layer optimization model includes an operator layer and a user layer.

[0061] The solution module is used to solve the master-slave game two-level optimization model using particle swarm optimization combined with integer linear programming. On the operator side, the purchase and sale price of electricity and heat is set according to the market supply and demand relationship, and the objective function is to maximize the revenue within a day; on the user side, the optimal output and load demand are determined according to the operator's price signals.

[0062] According to a third aspect of the embodiments of this application, a non-volatile storage device is provided, comprising: a processor, and a memory communicatively connected to the processor;

[0063] The memory stores computer-executed instructions;

[0064] The processor executes computer execution instructions stored in the memory to implement the method provided in the first aspect.

[0065] According to a fourth aspect of the embodiments of this application, a computer-readable storage medium is provided, wherein computer-executable instructions are stored in the computer-readable storage medium, and the computer-executable instructions are executed by a processor to implement the method provided in the first aspect.

[0066] According to a fifth aspect of the embodiments of this application, a computer program product is provided, including a computer program that, when executed by a processor, implements the method provided in the first aspect.

[0067] Beneficial effects:

[0068] This application discloses a collaborative optimization operation method for an integrated energy system in a fish farm. This method involves establishing models for biomass cogeneration, batteries, and thermal storage tanks within the integrated energy system. To address uncertainties on both the source and load sides, a fuzzy chance constraint method is used to relax deterministic constraints into system constraints containing fuzzy variables, and trapezoidal fuzzy parameters are used to clarify these constraints. A comprehensive demand response model is established. A master-slave game-themed bi-level optimization model for the integrated energy system is also established. The model is solved using a particle swarm optimization algorithm combined with integer linear programming. On the operator side, electricity and heat prices are set based on market supply and demand, with the objective function being to maximize daily revenue. On the user side, optimal output and load demand are determined based on the operator's price signals. This approach effectively balances the benefits for both the energy supply and user sides, fully leveraging the user's demand response potential and achieving economical, flexible, and collaborative optimization operation of the integrated energy system in the fish farm.

[0069] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0070] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application, and do not constitute an undue limitation of this application.

[0071] Figure 1 This is a flowchart illustrating an exemplary embodiment of a collaborative optimization operation method for a fish farm integrated energy system provided in this application.

[0072] Figure 2 This is a schematic diagram illustrating the solution process of a master-slave game two-layer optimization model according to an exemplary embodiment;

[0073] Figure 3 This is an example of an energy load versus wind and solar power output forecast curve.

[0074] Figure 4 This is a graph showing the relationship between ambient temperature and water temperature according to an exemplary embodiment;

[0075] Figure 5 This is a schematic diagram illustrating a power dispatch optimization result according to an exemplary embodiment;

[0076] Figure 6 This is a diagram illustrating the results of thermal energy scheduling optimization according to an exemplary embodiment;

[0077] Figure 7 This is a schematic diagram of the structure of a fish farm integrated energy system collaborative optimization operation device according to an exemplary embodiment. Detailed Implementation

[0078] To enable those skilled in the art to better understand the technical solutions of this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0079] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0080] This invention provides a method for the coordinated optimization operation of a fishery integrated energy system based on master-slave demand response and integrated demand response. The following will be discussed in conjunction with the appendix. Figure 1 ~Attached Figure 6 This invention provides a detailed description of a collaborative optimization operation method for a fish farm integrated energy system according to an embodiment of the present invention.

[0081] Figure 1 A flowchart illustrating a collaborative optimization operation method for a fish farm integrated energy system, provided as an exemplary embodiment of this application. Figure 1As shown, the specific steps of the collaborative optimization operation method for the integrated energy system of this fishery are as follows:

[0082] Step 110: Based on the biomass cogeneration, battery, thermal storage tank, and electric heating included in the integrated energy system, establish simulation models for biomass cogeneration, battery, thermal storage tank, and electric heating, respectively.

[0083] Furthermore, biomass cogeneration, which uses organic media for combustion heating, can achieve a utilization efficiency of 88%. Based on this, a biomass cogeneration model is established for use in integrated energy systems:

[0084]

[0085] in, Let α be the electrical power of CHP during time period t. e The electrical conversion efficiency (%) of the CHP system. η represents the biomass burned during time period t. b For biomass combustion conversion efficiency, f NCVb Δt represents the net calorific value of the biomass used, and Δt represents the scheduling time.

[0086] Furthermore, the thermal output of the biomass CHP system is: in, Let α be the thermal power of CHP during time period t. h The thermal conversion efficiency of the CHP system.

[0087] A battery simulation model is established for the batteries included in the integrated energy system as follows:

[0088]

[0089] in, It is the SOC value of the battery. satisfy and These are the charging and discharging power of the battery, respectively. satisfy

[0090] satisfy Δt is the time interval. and These are the charging and discharging status markers for the battery, respectively.

[0091] Furthermore, in actual operation, the battery must meet the charge / discharge ramp rate constraint shown in the following formula:

[0092]

[0093] in, and These represent the upper and lower limits of the ramp rate under the charging and discharging states of the battery, respectively.

[0094] Thermal storage tanks can store thermal energy when there is a surplus, and release it when there is a shortage of thermal energy or the cost of generating heat is high, thus improving the flexibility and economy of system operation. However, they must meet capacity constraints and thermal storage / release power constraints. Therefore, referring to the construction process of battery simulation models, a thermal storage tank simulation model is established for the thermal storage tank included in the integrated energy system as follows:

[0095]

[0096] in, For storing thermal energy in the heat storage tank, satisfy and These represent the heat storage and heat release power of the heat storage tank, respectively. satisfy satisfy η tst,chr The energy loss rate of the thermal storage tank. and These are the charging and discharging markers for the thermal storage tank. and satisfy

[0097] Similar to the operating mode of batteries, heat storage tanks also need to meet ramp rate constraints: and in, and These represent the upper and lower limits of the ramp rate under the charging and discharging states of the thermal storage tank, respectively.

[0098] The following simulation model for electric heating is established for the electric heating included in the integrated energy system:

[0099]

[0100] In the formula, Let η be the heat output and power consumption of the electric heating equipment at time t. rl For electric heating efficiency.

[0101] Step 120: To address the uncertainties on both sides of the source load, the deterministic constraints are relaxed into system constraints with fuzzy variables using the fuzzy chance constraint method, and the system constraints are clarified using trapezoidal fuzzy parameters.

[0102] Furthermore, to address the uncertainties on both the source and load sides, a fuzzy chance constraint method is adopted to introduce fuzzy parameters for wind power. Photovoltaic fuzzy parameters Load fuzzy parameters Fuzzy parameters for wind power are introduced using a fuzzy chance constraint method. Photovoltaic fuzzy parameters Load fuzzy parameters The triangular fuzzy parameters used in the process are: Where, P1=μ1P f,t, P2=μ2P f,t, P3=μ3P f,t , This is a fuzzy expression for wind power, solar power output, or load forecasting, where P1~P3 are the corresponding triangular membership parameters, μ1~μ3 are proportionality coefficients, and P... f,t Let t be the predicted value of photovoltaic, wind power, or load at time t.

[0103] The deterministic constraints are relaxed into system constraints containing fuzzy variables, and the system constraints are clarified using trapezoidal fuzzy parameters: During the model solution process, the system operation has high security requirements, so the confidence level should not be too low. Based on this, after fuzzy chance constraint processing, when the confidence level α ≥ 1 / 2, the system chance constraint P is clarified using trapezoidal fuzzy parameters. r The clear equivalence class {g(x,ξ)≤0}≥α represents the system clearing constraint as follows:

[0104]

[0105] in, Let's assume two functions, where h0(x) is a part of the function, and θ... k1 ~θ k3 Let be the membership parameter, k = 1, 2, ..., t, t ∈ R.

[0106] For this integrated energy system, the electrical and thermal balance constraints are as follows:

[0107]

[0108]

[0109] in, These are photovoltaic power output, wind power output, biomass cogeneration power generation, energy storage discharge, energy storage charging, purchasing electricity from the grid, selling electricity to the grid, and electricity consumption for heat generation. These are respectively: heat generation from biomass cogeneration, heat storage and absorption, heat storage and release, heat load, and heat generation from electric heating.

[0110] After fuzzy chance constraint processing, the original electrical and thermal power balance constraints are transformed using trapezoidal fuzzy parameters as shown in the following equation:

[0111]

[0112]

[0113] This invention considers the impact of source-load uncertainty on the system, adopts fuzzy chance constraints on the uncertainty of wind power output and load forecasting, improves wind power absorption, and studies and analyzes the impact of confidence level on the optimized operation of the system.

[0114] Step 130: Establish a comprehensive demand response model, which includes an electrical demand response model and a thermal demand response model.

[0115] The electricity demand response model is established as follows:

[0116]

[0117]

[0118]

[0119] in, These are the user-side fixed electrical load, flexible electrical load, and additional electrical load consumed due to electric heating. This represents the electrical load adjustment at time t. This represents the adjusted flexible electrical load. ε is the maximum allowable proportion of the electrical load adjustment at time t within a day, and k is the proportion of the total electrical load adjustment within a day. The larger ε and k are, the more flexible the electrical load adjustment is at each time period, and the greater the user-side demand response capability.

[0120] Fish are poikilothermic animals, meaning their body temperature changes with temperature. Therefore, temperature has a close relationship with their growth, development, and activity. Different fish species have different metabolic energy supply methods and thus possess a suitable temperature range for their own metabolism. Within this range, fish have a strong appetite and consume large amounts of food. Therefore, in fish farming, it is necessary to control the water temperature within a certain range. Considering the process of water temperature change, using indoor temperature as the state variable and heat demand as the heat load, a balance equation between heat demand and temperature is established: in, The load is a flexible heat load, where S is the heating area of ​​the fishing ground, C is the heat capacity per unit heating area, and ω is the heat dissipation coefficient due to the temperature difference between the inside and outside. The water temperature.

[0121] For flexible heat load regulation, the water temperature needs to fluctuate within the upper and lower limits of the temperature range acceptable to fish, thus satisfying the following constraints: in, and These are the lowest and highest room temperatures that fish can tolerate, respectively.

[0122] Furthermore, based on the balance equation between heat demand and temperature... and constraint relationships Establish a heat demand response model.

[0123] This invention considers the influence of temperature on the heat demand of fish and sets a temperature penalty factor for the optimal growth temperature of fish, and introduces a temperature-based fish comfort penalty term into the objective function.

[0124] Step 140: Establish a master-slave game two-layer optimization model for the integrated energy system. The master-slave game two-layer optimization model includes an operator layer and a user layer.

[0125] Furthermore, based on user-side load demand and market supply and demand, a pricing strategy is formulated, and an operator-level optimization model is established with maximizing revenue as the objective function:

[0126]

[0127] in, These are, respectively, the electricity transactions between microgrid operators and users, and the revenue generated from providing heating to users; C grid C refers to the costs incurred from transactions with the power grid. om For the comprehensive energy system operation and maintenance costs, C cchp For fuel costs, of which, and satisfy: Let be the electrical and thermal load power on the energy-consuming side at time t, respectively. These are the prices of electricity and heat sold to the energy-consuming side at time t, respectively. and The on-grid and retail electricity prices at time t are respectively, satisfying:

[0128]

[0129] Integrated energy system operation and maintenance costs adopt Calculate K i The operation and maintenance coefficients for each piece of equipment. The output power of each device; fuel cost C cchp use calculate, a represents the output electrical power of biomass cogeneration. e b e c e These represent the fuel cost coefficients for biomass cogeneration.

[0130] Based on the selling price provided by the integrated energy operator on the user side, the user optimizes their own electricity and heat load. The objective function is the difference between the user's utility function, energy cost, and fish temperature comfort penalty term. Therefore, using the difference between the user's utility function, energy cost, and fish temperature comfort penalty term as the objective function, the user-level optimization model is established as follows:

[0131] max C user =C sa -C energy -C pu

[0132] in, C sa v represents the user's utility function. e α e v h α h The coefficient representing the preference for electricity and heat consumption reflects users' energy demand preferences and influences the magnitude of demand. e α e v h α h These can be set to 1.8, 0.0012, 1.4, and 0.001 respectively. The prices at which operators sell electricity and heat to users; As a comfort penalty item, T represents the water temperature. in,set To set the optimal water temperature, ω represents the penalty factor, expressed in yuan / ℃. It characterizes the sensitivity of fish to changes in water temperature and is defined as the fish sensitivity coefficient. The larger the value of ω, the greater the penalty caused by temperature deviation, thus bringing the water temperature closer to the optimal temperature set by the fish, resulting in higher temperature comfort for them.

[0133] The embodiments of the present invention take into account the interaction of multiple energy sources in a comprehensive energy system, give full play to the complementarity and mutual assistance of multiple energy forms such as electricity and heat, and promote the balance of energy supply and demand.

[0134] Step 150: The particle swarm optimization algorithm combined with integer linear programming is used to solve the master-slave game two-level optimization model. The operator sets the purchase and sale price of electricity and heat based on the market supply and demand relationship, and the objective function is to maximize the revenue within a day; the user determines the optimal output and load demand based on the operator's price signal.

[0135] Further reference Figure 2As shown, the master-slave game model is solved using the particle swarm optimization algorithm and mixed integer linear programming. The master-slave game model of the integrated energy system is solved by calling the Cplex solver through the Yalmip toolbox. On the operator side, the purchase and sale price of electricity and heat is set according to the market supply and demand relationship, and the objective function is to maximize the revenue within a day. On the user side, the optimal output and load demand are determined according to the price signals of the operator.

[0136] Taking a certain fish farm integrated energy system as an example, this invention presents a simulation analysis of the collaborative optimization operation method of the fish farm integrated energy system. Typical daily predicted renewable energy output and predicted load are as follows: Figure 3 As shown in Table 1, the electricity purchase and sale by the power grid is as follows. The representative aquatic product of this fishery is the Chinese mitten crab (Mammoth mitten). Its body temperature changes with temperature, directly affecting its growth and reproduction. It begins to feed when the water temperature is above 6°C, and starts molting at 15°C. Molting is inhibited when the water temperature exceeds 32°C. Therefore, the minimum water temperature is set at 15°C, and the maximum should not exceed 32°C, with the optimal water temperature set at 26°C.

[0137] Table 1 Parameters of Power Grid Purchase and Sale

[0138] 0.00-9.00 0.4 0.35 10.00-12.00 0.8 0.35 13.00-16.00 1.2 0.35 17.00-20.00 0.8 0.35 21.00-24.00 1.2 0.35

[0139] 1) The impact of flexible electrical load on energy operator revenue is shown in Table 2. By changing the proportion of flexible electrical load, the corresponding user-side revenue and operator revenue are obtained. Under the condition of no temperature penalty factor and a confidence level of 0.9, the impact of different proportions of flexible electrical load on system revenue is studied.

[0140] Table 2 Comparison of different flexible electrical load proportions

[0141] 0 17369.13 5996.41 5 17717.68 6241.35 10 17931.96 6805.07

[0142] As shown in Table 2 above, with the increase in the proportion of flexible electrical load, the user-side adjustable electrical load becomes larger and more flexible, resulting in a greater demand response capability on the user side. Driven by electricity price incentives, in order to reduce the electricity purchase cost for users of the integrated energy system, the electrical load before and after demand response exhibits the characteristics of "peak shaving and valley filling." The table also shows that flexible electrical load is positively correlated with user-side revenue and operator revenue.

[0143] 2) The impact of the temperature penalty factor on system gains is shown in Table 3. (Refer to...) Figure 4 As shown, the impact of temperature penalty factors on operators and users is studied by changing the temperature penalty factor in the user-side objective function. Under a confidence level of 0.9 and a 10% flexible load ratio, the impact of different penalty factors on system revenue is investigated:

[0144] Table 3 Comparison results of different penalty factors

[0145] 0 17931.96 6805.07 25 17008.59 6767.17 50 16249.90 6758.95

[0146] As the penalty factor increases, the controlled indoor temperature gradually approaches the optimal temperature of 26°C, as shown in Table 3 above. The temperature penalty factor is negatively correlated with both the user-side objective function value and the operator's objective function. While the existence of the penalty factor results in some economic loss, it brings the water temperature closer to the optimal temperature for fish adaptation, which is beneficial for fish growth and reproduction. (Reference) Figure 4 As shown, the larger the temperature penalty factor, the greater the penalty caused by temperature deviation. When the water temperature is closer to the optimal temperature set by the fish, the fish will be more comfortable and more conducive to growth and reproduction.

[0147] The impact of confidence level on system benefits is shown in Table 4. Under the conditions of no temperature penalty factor and a 10% flexible load ratio, the impact of confidence level on system benefits is investigated.

[0148] Table 4 Comparison results at different confidence levels

[0149] 0.7 19332.34 6943.27 0.8 18117.94 6935.03 0.9 17931.96 6805.07

[0150] refer to Figure 5 and Figure 6 As shown, the confidence level reflects the decision-maker's ability to control risks and the system's security performance. As the confidence level increases, the system's ability to withstand risks improves, but the operator's objective function value continuously decreases, meaning that the system operation sacrifices economy to improve operational reliability.

[0151] This application discloses a collaborative optimization operation method for an integrated energy system in a fish farm. This method involves establishing simulation models for biomass cogeneration, batteries, thermal storage tanks, and electric heating, respectively, based on the components of the integrated energy system: biomass cogeneration, batteries, thermal storage tanks, and electric heating. To address uncertainties on both the source and load sides, a fuzzy chance constraint method is used to relax deterministic constraints into system constraints containing fuzzy variables, and trapezoidal fuzzy parameters are used to clarify these constraints. A comprehensive demand response model is established. A master-slave game-themed bi-level optimization model for the integrated energy system is also established. The master-slave game-themed bi-level optimization model is solved using a particle swarm optimization algorithm combined with integer linear programming. On the operator side, electricity and heat prices are set based on market supply and demand, with the objective function being to maximize daily revenue. On the user side, optimal output and load demand are determined based on the operator's price signals. This approach effectively balances the revenue of both the energy supply and user sides, fully leveraging the user's demand response potential and achieving economical, flexible, and collaborative optimization operation of the integrated energy system in the fish farm.

[0152] Figure 7This is a schematic diagram of a collaborative optimization operation device for a fishery integrated energy system, provided as an exemplary embodiment of this application. The collaborative optimization operation device for a fishery integrated energy system provided in this embodiment can execute the processing flow provided in an embodiment of a collaborative optimization operation method for a fishery integrated energy system. Figure 7 As shown, the integrated energy system collaborative optimization operation device 20 for fisheries provided in this application includes:

[0153] The model building module 201 is used to establish biomass cogeneration, battery simulation model, thermal storage tank simulation model and electric heating simulation model respectively based on the biomass cogeneration, battery, thermal storage tank and electric heating included in the integrated energy system.

[0154] The determination module 202 is used to relax the deterministic constraints into system constraints with fuzzy variables by using the fuzzy chance constraint method to address the uncertainties on both sides of the source load, and to clarify the system constraints by using trapezoidal fuzzy parameters.

[0155] Processing module 203 is used to establish a comprehensive demand response model, which includes an electrical demand response model and a thermal demand response model.

[0156] Optimization module 204 is used to establish a master-slave game two-layer optimization model for the integrated energy system. The master-slave game two-layer optimization model includes an operator layer and a user layer.

[0157] The solution module 205 is used to solve the master-slave game two-level optimization model using the particle swarm optimization algorithm combined with the integer linear programming method. On the operator side, the purchase and sale price of electricity and heat is set according to the market supply and demand relationship, and the objective function is to maximize the revenue within a day; on the user side, the optimal output and load demand are determined according to the operator's price signal.

[0158] The apparatus provided in this application embodiment can be specifically used to perform the above-described... Figure 1 The specific functions and technical effects of the solutions provided in the corresponding method embodiments will not be elaborated here.

[0159] This invention also provides a non-volatile storage device comprising: a processor, and a memory communicatively connected to the processor;

[0160] The memory stores instructions that the computer executes;

[0161] The processor executes computer execution instructions stored in the memory to implement the solution provided in any of the above method embodiments; the specific functions and technical effects achieved are not elaborated here. The electronic device can be the server mentioned above.

[0162] This application also provides a computer-readable storage medium storing computer-executable instructions. When executed by a processor, the computer-executable instructions are used to implement the solution provided in any of the above method embodiments. The specific functions and technical effects to be achieved are not described here.

[0163] This application also provides a computer program product, which includes a computer program stored in a readable storage medium. At least one processor of the electronic device can read the computer program from the readable storage medium. The at least one processor executes the computer program to cause the electronic device to perform the solution provided in any of the above method embodiments. The specific functions and technical effects that can be achieved are not described here.

[0164] The application scenarios described in this application are for the purpose of more clearly illustrating the technical solutions of this application, and do not constitute a limitation on the technical solutions provided in this application. As those skilled in the art will know, with the emergence of new application scenarios, the technical solutions provided in this application are also applicable to similar technical problems.

[0165] Those skilled in the art will understand that various aspects of this application can be implemented as a system, method, or program product. Therefore, various aspects of this application can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, collectively referred to herein as a "circuit," "module," or "system."

[0166] In some possible implementations, the electronic device according to this application may include at least one processor and at least one memory. The memory stores program code that, when executed by the processor, causes the processor to perform the operational data management methods according to the various exemplary embodiments of this application described above. For example, the processor may perform steps such as those in the operational data management method.

[0167] It should be noted that although several units or sub-units of the device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of this application, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided and embodied by multiple units.

[0168] Furthermore, although the operations of the method of this application are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0169] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0170] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable image scaling device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable image scaling device, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0171] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable image scaling device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0172] These computer program instructions can also be loaded onto a computer or other programmable image scaling device, causing a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable device for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0173] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0174] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for the coordinated optimization operation of a comprehensive energy system in a fish farm, characterized in that, The method includes: Based on the biomass cogeneration, batteries, thermal storage tanks, and electric heating included in the integrated energy system, respectively establish biomass cogeneration model, battery simulation model, thermal storage tank simulation model, and electric heating simulation model; To address the uncertainties on both sides of the source load, a fuzzy chance constraint method is used to relax the deterministic constraints into system constraints with fuzzy variables, and trapezoidal fuzzy parameters are used to clarify the system constraints. Establish a comprehensive demand response model, which includes an electricity demand response model and a heat demand response model; The electricity demand response model is established as follows: in, , , These are the user-side fixed electrical load, flexible electrical load, and additional electrical load consumed due to electric heating. This represents the electrical load adjustment at time t. This indicates the adjusted flexible electrical load. This represents the maximum permissible percentage of electrical load adjustment at time t within a day. The proportion of the total daily electricity load adjustment; According to the balance equation of heat demand and temperature and constraint relationships Establish a heat demand response model ,in, To maintain a constant heat load, For flexible heat load, Heating area for fishing grounds and waters The heat capacity per unit heating area. The coefficient of heat dissipation due to the temperature difference between the inside and outside. The water temperature and These are the lowest and highest room temperatures that fish can tolerate, respectively. A master-slave game-themed two-layer optimization model for an integrated energy system is established, which includes an operator layer and a user layer. Among them, based on user-side load demand and market supply and demand, a pricing strategy is formulated, and an operator-level optimization model is established with maximizing revenue as the objective function: in, , These are electricity transactions between microgrid operators and users, and revenue generated from providing heating to users. These are the fees incurred from transactions with the power grid. For the comprehensive energy system operation and maintenance costs, For fuel costs, of which, and satisfy , , Let be the electrical and thermal load power on the energy-consuming side at time t, respectively. , These are the prices of electricity and heat sold to the energy-consuming side at time t, respectively. and The on-grid and retail electricity prices at time t are respectively, satisfying... The integrated energy system operation and maintenance costs adopt the following approach: calculate, The operation and maintenance coefficients for each piece of equipment. Output power of each device; fuel cost use calculate, This indicates the output electrical power of the biomass cogeneration. , , These represent the fuel cost coefficients for biomass cogeneration; Using the difference between the user's utility function, energy cost, and fish temperature comfort penalty as the objective function, a user-level optimization model is established as follows: in, , , , Represents the user's utility function. , , , This represents the preference coefficient for consuming electricity and heat. , The prices at which operators sell electricity and heat to users; As a comfort penalty item, The water temperature To set the optimal water temperature, Indicates the penalty factor; The particle swarm optimization algorithm combined with integer linear programming is used to solve the master-slave game two-level optimization model. On the operator side, the purchase and sale price of electricity and heat is set according to the market supply and demand relationship, and the objective function is to maximize the revenue within a day; on the user side, the optimal output and load demand are determined according to the operator's price signal.

2. The method according to claim 1, characterized in that, The establishment of biomass cogeneration, battery, thermal storage tank, and electric heating simulation models based on the biomass cogeneration, battery, thermal storage tank, and electric heating systems included in the integrated energy system includes: The biomass cogeneration model for integrated energy systems is as follows: in, The electrical power of CHP during time period t. The thermal power of CHP during time period t. The electrical conversion efficiency (%) of the CHP system. For the thermal conversion efficiency of the CHP system, The biomass burned during time period t. For biomass combustion conversion efficiency, The net calorific value of the biomass used. For scheduling time; A battery simulation model is established for the batteries included in the integrated energy system as follows: in, It is the SOC value of the battery. satisfy ; and These are the charging and discharging power of the battery, respectively. satisfy ; satisfy ; It is a time interval. and These are the charging and discharging status markers for the battery, respectively. ; A simulation model of the thermal storage tank included in the integrated energy system is established as follows: in, For storing thermal energy in the heat storage tank, satisfy ; and These represent the heat storage and heat release power of the heat storage tank, respectively. satisfy ; satisfy ; The energy loss rate of the thermal storage tank. and These are the charging and discharging markers for the thermal storage tank. and satisfy ; The following simulation model for electric heating is established for the electric heating included in the integrated energy system: In the formula, , for The heat output and power consumption of the electric heating equipment are constantly monitored. For electric heating efficiency.

3. The method according to claim 1 or 2, characterized in that, To address the uncertainties on both sides of the source load, the method employs fuzzy chance constraints to relax deterministic constraints into system constraints containing fuzzy variables, and utilizes trapezoidal fuzzy parameters to clarify the system constraints, including: To address the uncertainties on both the source and load sides, a fuzzy chance constraint method is used to introduce fuzzy parameters for wind power. Photovoltaic fuzzy parameters fuzzy load parameters ; After fuzzy chance constraint processing, when the confidence level When ≥1 / 2, the system opportunity constraint is applied using trapezoidal fuzzy parameters. Clear equivalence classes are the system clearing constraints as follows: in, , For the two assumed functions, As part of a function, ~ For membership parameters, This is the membership parameter.

4. The method according to claim 3, characterized in that, Fuzzy parameters for wind power are introduced using a fuzzy chance constraint method. Photovoltaic fuzzy parameters fuzzy load parameters The triangular fuzzy parameters used in the process are: ,in, , For fuzzy expressions used to predict wind power, solar power output, or load, ~ For the corresponding triangular membership parameters, ~ This is the proportionality coefficient. Let t be the predicted value of photovoltaic, wind power, or load at time t.

5. The method according to claim 4, characterized in that, Using trapezoidal fuzzy parameters to clarify system constraints also includes: Convert the electrical and thermal power balance constraint into: in, , , , , , , , , These are, respectively, at time t: photovoltaic power output, wind power output, biomass cogeneration power generation, energy storage discharge, energy storage charging, purchasing electricity from the grid, selling electricity to the grid, and electricity consumption for heating. , , , , These represent the heat generation, heat storage absorption, heat storage release, heat load, and heat generation from electric heating at time t, respectively.

6. The method according to claim 1, characterized in that, The method of using particle swarm optimization combined with integer linear programming to solve the master-slave game two-level optimization model includes: The master-slave game model is solved using the particle swarm optimization algorithm and mixed-integer linear programming. The master-slave game model of the integrated energy system is solved by calling the Cplex solver through the Yalmip toolbox. On the operator side, the purchase and sale price of electricity and heat is set according to the market supply and demand relationship, and the objective function is to maximize the revenue within a day. On the user side, the optimal output and load demand are determined according to the price signals from the operator.

7. A collaborative optimization operation device for a fishery integrated energy system, used to implement the collaborative optimization operation method for a fishery integrated energy system as described in any one of claims 1-6, characterized in that, The integrated energy system for the fish farm includes a collaborative optimization operation device: The model building module is used to establish biomass cogeneration, battery, thermal storage tank, and electric heating simulation models based on the biomass cogeneration, battery, thermal storage tank, and electric heating systems included in the integrated energy system. The determination module is used to relax deterministic constraints into system constraints with fuzzy variables by using the fuzzy chance constraint method to address uncertainties on both sides of the source load, and to clarify the system constraints by using trapezoidal fuzzy parameters. The processing module is used to establish a comprehensive demand response model, which includes an electrical demand response model and a thermal demand response model. The optimization module is used to establish a master-slave game two-layer optimization model for the integrated energy system. The master-slave game two-layer optimization model includes an operator layer and a user layer. The solution module is used to solve the master-slave game two-level optimization model using particle swarm optimization combined with integer linear programming. On the operator side, the purchase and sale price of electricity and heat is set according to the market supply and demand relationship, and the objective function is to maximize the revenue within a day; on the user side, the optimal output and load demand are determined according to the operator's price signals.

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