Multi-time scale optimization scheduling method and device for integrated energy system

Abstract

The invention relates to a multi-time-scale optimization scheduling method and device for an integrated energy system, and belongs to the technical field of power system optimization control, and the method comprises the steps: obtaining historical scheduling data and load of the integrated energy system, and obtaining a day-ahead optimal scheduling plan of the integrated energy system at the minimum cost based on the historical scheduling data and load; constructing a state space prediction model and an intra-day scheduling model of the integrated energy system, wherein the intra-day scheduling model comprises a first objective function and constraint conditions; constructing a time domain rolling optimization model based on the state space prediction model according to the day-ahead optimal scheduling plan, the constraint condition and the first objective function, and solving the time domain rolling optimization model to obtain an intra-day scheduling plan; and correcting the state of the integrated energy system according to the intra-day scheduling plan to obtain a corrected intra-day scheduling plan. According to the method, the demand price corresponding to the response load is considered, and the load of the integrated energy system is predicted and dynamically adjusted through the MPC control algorithm, so that the load configuration efficiency of the integrated energy system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system optimization control, and in particular to a multi-time scale optimization scheduling method and device for an integrated energy system. Background Art

[0002] In the context of today's global energy transformation, integrated energy systems have become an important way to achieve efficient energy utilization, promote the integration of renewable energy and ensure energy security.

[0003] As the proportion of renewable energy generation continues to grow, its inherent intermittency and uncertainty have placed higher demands on the stable operation of the power grid. At the same time, the diversification and dynamic changes in user-side demand patterns have further exacerbated the complexity of energy supply and demand matching. With the global commitment to reducing greenhouse gas emissions and achieving sustainable development goals, the rapid development of renewable energy (such as wind power and solar energy) has become an inevitable trend. However, the randomness and intermittency of this type of energy requires the energy system to have greater flexibility and regulation capabilities. By configuring energy storage devices of different energy types, the volatility of the system can be effectively reduced and the stability can be enhanced.

[0004] Problems existing in the prior art are: first, the scheduling plan does not take into account the demand response strategy, which may cause excessive pressure on the power grid operation, increase instability, and reduce the efficiency of market resource allocation; second, the scheduling plan within the day cannot be optimized according to the real-time scheduling situation, resulting in the overall scheduling plan being unable to coordinate the equipment of the entire system, resulting in poor scheduling effect.

[0005] In summary, the integrated energy system in the existing technology lacks an efficient scheduling method to improve the efficiency of energy allocation. Summary of the invention

[0006] In view of this, it is necessary to provide a multi-time scale optimization scheduling method and device for an integrated energy system to achieve the purpose of improving the configuration efficiency of the integrated energy system.

[0007] In order to achieve the above object, the present invention provides a multi-time scale optimization scheduling method for an integrated energy system, comprising: Obtain and obtain the optimal day-ahead dispatch plan of the integrated energy system at the minimum cost based on the historical dispatch data and response load of the integrated energy system; Construct a state space prediction model and an intraday scheduling model for the integrated energy system. The intraday scheduling model includes the first objective function and constraints. According to the optimal dispatch plan of the day ahead, the constraints and the first objective function, a time domain rolling optimization model is constructed and solved based on the state space prediction model to obtain the intraday dispatch plan of the load; The state of the integrated energy system is corrected according to the intraday dispatch plan to obtain a corrected load intraday dispatch plan.

[0008] In a possible implementation, the acquiring and obtaining the day-ahead optimal dispatching plan of the integrated energy system at minimum cost based on the load historical dispatching data and the response load of the integrated energy system includes: Acquire historical load dispatch data and response load of the integrated energy system, wherein the response load includes heat load, cooling load and electric load; Building a day-ahead dispatching model based on the response load of the integrated energy system, wherein the day-ahead dispatching model includes a second objective function and constraint conditions; The second objective function is solved based on the load historical dispatch data and constraint conditions to obtain the day-ahead optimal dispatch plan at the minimum cost of the integrated energy system.

[0009] In a possible implementation, obtaining the response load of the integrated energy system includes: Establish a price elasticity matrix of comprehensive demand response adjustment coefficients based on price elasticity for the integrated energy system; The response load of the integrated energy system is obtained based on the price elasticity matrix.

[0010] In a possible implementation, the expression of the second objective function is:

[0011]

[0012] In the formula, F 1 is the total operating cost of the wind, solar and hydrogen integrated energy system; F grid (t) Indicates the cost of electricity purchase and sale for the entire system; F op (t) The operation and maintenance cost of the entire system; F gas (t) is the fuel cost of the entire system; F buy (t) is the electricity purchase cost, F sell (t) F is the income from electricity sales; PV (t) is the operation and maintenance cost of photovoltaic equipment, F WT (t) is the operation and maintenance cost of wind power equipment, F HP (t) is the operation and maintenance cost of heat pump equipment, F GT (t) The operation and maintenance cost of gas turbine equipment isF GB (t) For gas boiler equipment operation and maintenance costs, F AC (t) is the operation and maintenance cost of the electric refrigerator equipment, F H (t) is the operation and maintenance cost of thermal energy storage equipment, F B (t) is the battery equipment operation and maintenance cost, F HS (t) is the operation and maintenance cost of hydrogen energy storage equipment, F EL (t) is the operation and maintenance cost of the electrolyzer equipment, F HFC (t) Operation and maintenance costs of hydrogen fuel cell equipment; F GT,gas (t) is the gas turbine fuel cost, F GB,gas (t) Fuel cost for gas boiler.

[0013] In a possible implementation, the constraints include energy supply and demand balance constraints, equipment operation constraints, and electricity / gas purchase and sale constraints: The energy supply and demand balance constraints include: electric energy balance constraints, heat balance constraints, cooling balance constraints, hydrogen balance constraints, and natural gas balance constraints; The equipment operation constraints include: photovoltaic and wind power output constraints, combined cooling, heating, electricity and hydrogen output constraints, and energy storage equipment constraints; The expression of the power balance constraint is:

[0014] In the formula, P load,e (t) for t The electrical load power at the moment, P buy,e (t) , P sell,e (t) are t Purchase and sell electricity at all times; P WT (t) , P PV (t) are the photovoltaic and wind power outputs at time t, P HP,e (t) , P AC,e (t) They aret The power consumption of heat pumps and electric refrigerators at all times, P GT (t) , P HFC (t) They are t The output power of gas turbines and hydrogen fuel cells at all times, P EL,con (t) for t The power consumption of the electrolytic cell at all times; The expression of the heat balance constraint is:

[0015] In the formula, P load,h (t) for t Heat load power at the moment, P HP (t) is the heating power of the heat pump at time t; The expression of the cooling balance constraint is:

[0016] In the formula, P load,c (t ) is the cooling load power at time t, P AC (t) is the cooling power of the electric refrigerator at time t; The expression of the hydrogen balance constraint is:

[0017] In the formula, P EL (t) for t The hydrogen production power of the electrolyzer at the moment, P HFC,H 2(t) for t The hydrogen power consumption of hydrogen fuel cells at all times; The expression of the natural gas balance constraint is:

[0018] In the formula, P buy,gas (t) is the gas purchasing power at time t, P GT,gas (t) is the gas consumption power of the gas turbine at time t, P GB,gas (t) is the gas consumption power of the gas boiler at time t; The expression of the photovoltaic and wind power output constraints is:

[0019] In the formula, Pmax PV The upper limit of photovoltaic power generation output , Pmax WT The upper limit of wind power output; The expression of the combined cooling, heating, electricity and hydrogen output constraint is:

[0020] In the formula, Pmin HP , Pmin AC , Pmin GT , Pmin HFC , Pmin EL , Pmin GB They are the lower limits of input power for heat pumps, electric refrigerators, gas turbines, hydrogen fuel cells, electrolyzers, and gas boilers. Pmax HP , Pmax AC , Pmax GT , Pmax HFC , Pmax EL , Pmax GB They are the upper limits of input power for heat pumps, electric refrigerators, gas turbines, hydrogen fuel cells, electrolyzers, and gas boilers; The expression of the energy storage device constraint is:

[0021]

[0022] In the formula, Smin H, Smax H, S H （t） are the lower and upper limits of the heat storage capacity of the heat storage tank and the heat storage state of the heat storage tank at time t, respectively. Smin B, Smax B, S B （t） are the lower and upper limits of the battery energy storage capacity and the energy storage state of the battery at time t, respectively. Smin HS, Smax HS, S HS （t） are the lower and upper limits of the heat storage capacity of the hydrogen storage tank and the hydrogen storage state of the hydrogen storage tank at time t, respectively. Pmax H, Pmax B, Pmax HS They are the maximum charging and discharging power of the heat storage tank, battery, and hydrogen storage tank, respectively. μ H , μ B , μ HS It is a 0-1 variable, which means that the energy storage device cannot be charged or discharged at the same time. When the value is 0, it means that the energy storage device is discharging energy, and when the value is 1, it means that the energy storage device is charging. The expression of the purchase and sale of electricity / gas constraints is:

[0023] In the formula, Pmax grid , Pmax gas The power limits for interacting with the power grid and gas grid are , μ grid It is a 0-1 variable, indicating that the purchase and sale of electricity cannot be restricted at the same time. When the value is 0, it means selling electricity to the grid, and when the value is 1, it means purchasing electricity from the grid.

[0024] In a possible implementation, the expression of the first objective function is:

[0025] In the formula, For the prediction time domain, for Always The power and storage status of each device at any time. for Always Reference values ​​for the power and storage status of each device at a given time and day.

[0026] In a possible implementation, the state space prediction model is expressed as:

[0027] In the formula, X(k) represents the state variable of the integrated energy system, which refers to the energy level of the energy storage device at the current moment and the working status of different devices; Y(k) represents the output variable of the integrated energy system, which refers to the energy level of the energy storage device and the actual load provided; u(k) represents the input variable of the integrated energy system, which refers to various loads, electricity price, cooling price, heating price, and natural gas price; d(k) represents the disturbance variable of the integrated energy system, which refers to the change in the utilization power of renewable energy; A, B, C, and D are the matrices of the state space prediction model; The expression of the time domain rolling optimization model is:

[0028] In the formula, For the prediction time domain, for Always +The predicted value at time i, is the time t + Reference value at time i, is the output bias weight coefficient, is the control weight coefficient; The correction formula for correcting the state of the integrated energy system according to the intraday dispatch plan is:

[0029] In the formula, is the error between the predicted value and the actual value, is the error coefficient matrix.

[0030] On the other hand, the present invention also provides a multi-time scale optimization scheduling device for an integrated energy system, comprising: The day-ahead dispatch plan acquisition module is used to acquire and obtain the day-ahead optimal dispatch plan of the integrated energy system at the minimum cost based on the load historical dispatch data and response load of the integrated energy system; A model building module is used to build a state space prediction model and an intraday scheduling model for the integrated energy system. The intraday scheduling model includes a first objective function and constraints. The intraday dispatch plan acquisition module is used to construct and solve the time domain rolling optimization model based on the state space prediction model according to the optimal dispatch plan of the day, the constraints and the first objective function, so as to obtain the intraday dispatch plan of the load; The revised intraday dispatch plan acquisition module is used to correct the state of the integrated energy system according to the intraday dispatch plan to obtain a revised intraday dispatch plan for the load.

[0031] On the other hand, the present invention also provides an electronic device, including a memory and a processor, wherein: The memory is used to store programs; The processor is coupled to the memory and is used to execute the program stored in the memory to implement the steps in a multi-time scale optimization scheduling method for an integrated energy system described in any one of the above implementation methods.

[0032] On the other hand, the present invention also provides a computer-readable storage medium for storing computer-readable programs or instructions, which, when executed by a processor, can implement the steps of a multi-time-scale optimization scheduling method for an integrated energy system described in any of the above-mentioned implementation methods.

[0033] The beneficial effects of the present invention are as follows: a multi-time scale optimization scheduling method for an integrated energy system provided by the present invention includes obtaining and based on the historical scheduling data and response load of the load of the integrated energy system, obtaining the optimal scheduling plan of the day before the integrated energy system at the minimum cost, taking the response load as an influencing factor to fully capture the dynamic characteristics of energy supply and demand and price fluctuations, and constructing a state space prediction model and an intraday scheduling model of the integrated energy system. The intraday scheduling model includes a first objective function and a constraint condition, and according to the optimal scheduling plan of the day before, the constraint condition and the first objective function, a time domain rolling optimization model is constructed and solved based on the state space prediction model to obtain the intraday scheduling plan of the load, and the state of the integrated energy system is corrected according to the intraday scheduling plan to obtain the corrected load intraday scheduling plan, thereby predicting the state of multiple time steps in the future through the model predictive control model, and adjusting the control strategy in real time to adapt to short-term and long-term energy demand changes. The present invention considers the demand price corresponding to the response load, and predicts and dynamically adjusts the load of the integrated energy system through the MPC control algorithm, thereby improving the load configuration efficiency of the integrated energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 A flowchart of an embodiment of a multi-time scale optimization scheduling method for an integrated energy system provided by the present invention; Figure 2 Provides a structural diagram of an integrated energy system for embodiments of the present invention; Figure 3 For the present invention Figure 1 A schematic flow chart of an embodiment of S102; Figure 4 An optimization result of electric load demand response in an embodiment of a multi-time scale optimization scheduling method for an integrated energy system provided by the present invention; Figure 5 The heat load demand response optimization result in one embodiment of a multi-time scale optimization scheduling method for a comprehensive energy system provided by the present invention; Figure 6 The cooling load demand response optimization result in one embodiment of a multi-time scale optimization scheduling method for an integrated energy system provided by the present invention; Figure 7 A schematic diagram of model predictive control rolling optimization shown in an embodiment of a multi-time scale optimization scheduling method for an integrated energy system provided by the present invention; Figure 8 An electric load power balance diagram in the day-ahead scheduling phase in an embodiment of a multi-time-scale optimization scheduling method for an integrated energy system provided by the present invention; Fig. 9A heat load power balance diagram in the day-ahead scheduling phase in an embodiment of a multi-time-scale optimization scheduling method for an integrated energy system provided by the present invention; Fig.10 A cooling load power balance diagram in the day-ahead scheduling phase in an embodiment of a multi-time-scale optimization scheduling method for an integrated energy system provided by the present invention; Fig.11 An electric load power balance diagram in the intraday scheduling stage in an embodiment of a multi-time scale optimization scheduling method for an integrated energy system provided by the present invention; Fig.12 A heat load power balance diagram in the intraday scheduling stage in one embodiment of a multi-time scale optimization scheduling method for a comprehensive energy system provided by the present invention; Fig.13 A cooling load power balance diagram in the intraday scheduling stage in one embodiment of a multi-time scale optimization scheduling method for an integrated energy system provided by the present invention; Fig.14 A schematic flow chart of an embodiment of a multi-time scale optimization scheduling device for an integrated energy system provided by the present invention; Fig.15 A schematic structural diagram of an embodiment of an electronic device provided by the present invention. DETAILED DESCRIPTION

[0035] The preferred embodiments of the present invention are described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not used to limit the scope of the present invention.

[0036] The present invention provides a multi-time scale optimization scheduling method and device for an integrated energy system, which are described below respectively.

[0037] Figure 1 A schematic flow chart of an embodiment of the multi-time scale optimization scheduling method for the integrated energy system provided by the present invention is as follows: Figure 1 As shown in Figure 1, the multi-time scale optimal scheduling of the integrated energy system includes: S101, obtaining and obtaining the optimal day-ahead dispatching plan of the integrated energy system at minimum cost based on the historical dispatching data of the load and the response load of the integrated energy system; S102, constructing a state space prediction model and an intraday scheduling model for the integrated energy system, wherein the intraday scheduling model includes a first objective function and constraints; S103, constructing and solving a time domain rolling optimization model based on a state space prediction model according to the optimal scheduling plan of the day ahead, constraints and the first objective function, and obtaining an intraday scheduling plan for the load; S104. Correct the state of the integrated energy system according to the intraday dispatch plan to obtain a corrected load intraday dispatch plan.

[0038] Compared with the prior art, the multi-time scale optimization scheduling method of the integrated energy system provided in this embodiment includes obtaining and obtaining the optimal scheduling plan of the day under the minimum cost of the integrated energy system based on the load historical scheduling data and the response load of the integrated energy system, taking the response load as an influencing factor to fully capture the dynamic characteristics of energy supply and demand and price fluctuations, and constructing a state space prediction model and an intraday scheduling model of the integrated energy system. The intraday scheduling model includes a first objective function and a constraint condition. According to the optimal scheduling plan of the day, the constraint condition and the first objective function, a time domain rolling optimization model is constructed and solved based on the state space prediction model to obtain the intraday scheduling plan of the load, and the state of the integrated energy system is corrected according to the intraday scheduling plan to obtain the corrected load intraday scheduling plan, so as to predict the state of multiple time steps in the future through the model predictive control model, and adjust the control strategy in real time to adapt to short-term and long-term energy demand changes. The present invention considers the demand price corresponding to the response load, and predicts and dynamically adjusts the load of the integrated energy system through the MPC control algorithm, thereby improving the load configuration efficiency of the integrated energy system.

[0039] It should be noted that if Figure 2 Shown is a structural diagram of the comprehensive energy system provided by an embodiment of the present invention.

[0040] In some embodiments of the present invention, in step S101, Figure 3 As shown, the acquisition and based on the load historical dispatching data and response load of the integrated energy system, the optimal dispatching plan of the day ahead under the minimum cost of the integrated energy system is obtained, including: S301, obtaining historical load dispatch data and response load of the integrated energy system, wherein the response load includes heat load, cooling load and electric load; S302, constructing a day-ahead dispatching model based on the response load of the integrated energy system, wherein the day-ahead dispatching model includes a second objective function and constraint conditions; S303: Solve the second objective function based on the load historical dispatch data and constraint conditions to obtain the day-ahead optimal dispatch plan at the minimum cost of the integrated energy system.

[0041] In some embodiments of the present invention, obtaining a response load of a comprehensive energy system includes: Establish a price elasticity matrix of comprehensive demand response adjustment coefficients based on price elasticity for the integrated energy system; The response load of the integrated energy system is obtained based on the price elasticity matrix.

[0042] In a specific embodiment of the present invention, a demand response model based on demand price elasticity is established, including three price-type demand models: electricity price, heating price, and cooling price. The elasticity coefficients of electricity price, heating price, and cooling price are defined. The calculation formula of the elasticity coefficient is:

[0043] in, is the elastic coefficient, is the change in load Q, For price The amount of change.

[0044] Generally speaking, price-based demand response is divided into two periods, one of which is the demand response that only considers a single period of time, which is called a single period, and the other is the demand response that considers multiple time intervals within a day, which is called a multi-period. Users are only affected by prices in a certain period of time, and are not affected by prices in any time period outside this period, which is called a single-period response. The user side will be affected by prices in multiple periods and change load habits, which is called a multi-period response. The multi-period response compares the actual electricity consumption of users in reality. In the multi-period response model, there are two elasticity coefficients: self-elasticity and cross-elasticity. The behavior of users making changes based on prices in the current period is called the self-elasticity coefficient, and the behavior of users making changes based on prices in multiple time periods is called the cross-elasticity coefficient. According to the definition of the elasticity coefficient in the above formula, the formula is expressed as follows:

[0045] in, for i The self-elasticity coefficient at time for j The cross elastic coefficient at time , and i ≠ j .

[0046] The expressions of the user-side response behavior to prices in multiple time periods are as follows:

[0047] in, is the change in electricity / heating / cooling price at a certain time before demand response; P is the price after electricity / heating / cooling price is adjusted at a certain time after demand response; is the change caused by the load change at a certain time before the demand response; Q is the load after the load change at a certain time after the demand response; E is the price elasticity matrix. The formula of the price elasticity matrix E is:

[0048] According to the above definitions of price elasticity coefficient and elasticity matrix, the load after demand response under time-of-use electricity price, time-of-use heating price and time-of-use cooling price can be obtained as follows:

[0049] Where P load P1, P2, ... P are the loads of electricity, heat and cooling after demand response. t is the load at time t, such as Figure 3 The results of the optimization of the electric load demand response are shown in Figure 4 The results of heat load demand response optimization are shown in Figure 5 The cooling load demand response optimization results are shown.

[0050] In a specific embodiment of the present invention, the day-ahead optimization scheduling is a day-ahead optimization scheduling model that predicts data for 24 time periods every hour on the next day on the previous day. The day-ahead optimization scheduling aims to determine the utilization of wind and solar energy at the hourly level on the next day, the operation plans of different energy supply equipment, and the operation plans of different energy storage equipment based on historical wind power and photovoltaic data through the day-ahead optimization scheduling model. The specific optimization scheduling model is to minimize the system cost of the entire system, including the cost of purchasing and selling electricity and the operation and maintenance cost of each device. The specific public function is the second objective function.

[0051] In some embodiments of the present invention, the expression of the second objective function is:

[0052]

[0053] In the formula, F 1 is the total operating cost of the wind, solar and hydrogen integrated energy system; F grid (t) Indicates the cost of electricity purchase and sale for the entire system; F op (t) The operation and maintenance cost of the entire system; F gas (t) is the fuel cost of the entire system; F buy (t) is the electricity purchase cost, F sell (t) F is the income from electricity sales; PV (t) is the operation and maintenance cost of photovoltaic equipment, F WT (t) is the operation and maintenance cost of wind power equipment, F HP (t) is the operation and maintenance cost of heat pump equipment, F GT (t)The operation and maintenance cost of gas turbine equipment is F GB (t) For gas boiler equipment operation and maintenance costs, F AC (t) is the operation and maintenance cost of the electric refrigerator equipment, F H (t) is the operation and maintenance cost of thermal energy storage equipment, F B (t) is the battery equipment operation and maintenance cost, F HS (t) is the operation and maintenance cost of hydrogen energy storage equipment, F EL (t) is the operation and maintenance cost of the electrolyzer equipment, F HFC (t) Operation and maintenance costs of hydrogen fuel cell equipment; F GT,gas (t) is the gas turbine fuel cost, F GB,gas (t) Fuel cost for gas boiler.

[0054] In some embodiments of the present invention, the constraints include energy supply and demand balance constraints, equipment operation constraints, and electricity / gas purchase and sale constraints: The energy supply and demand balance constraints include: electric energy balance constraints, heat balance constraints, cooling balance constraints, hydrogen balance constraints, and natural gas balance constraints; The equipment operation constraints include: photovoltaic and wind power output constraints, combined cooling, heating, electricity and hydrogen output constraints, and energy storage equipment constraints; The expression of the power balance constraint is:

[0055] In the formula, P load,e (t) for t The electrical load power at the moment, P buy,e (t) , P sell,e (t) are t Purchase and sell electricity at all times; P WT (t) , P PV (t) are the photovoltaic and wind power outputs at time t, P HP,e (t) , P AC,e(t) They are t The power consumption of heat pumps and electric refrigerators at all times, P GT (t) , P HFC (t) They are t The output power of gas turbines and hydrogen fuel cells at all times, P EL,con (t) for t The power consumption of the electrolytic cell at all times; The expression of the heat balance constraint is:

[0056] In the formula, P load,h (t) for t Heat load power at the moment, P HP (t) is the heating power of the heat pump at time t; The expression of the cooling balance constraint is:

[0057] In the formula, P load,c (t ) is the cooling load power at time t, P AC (t) is the cooling power of the electric refrigerator at time t; The expression of the hydrogen balance constraint is:

[0058] In the formula, P EL (t) for t The hydrogen production power of the electrolyzer at the moment, P HFC,H 2(t) for t The hydrogen power consumption of hydrogen fuel cells at all times; The expression of the natural gas balance constraint is:

[0059] In the formula, P buy,gas (t) is the gas purchasing power at time t, P GT,gas (t) is the gas consumption power of the gas turbine at time t, P GB,gas (t)is the gas consumption power of the gas boiler at time t; The expression of the photovoltaic and wind power output constraints is:

[0060] In the formula, Pmax PV The upper limit of photovoltaic power generation output , Pmax WT The upper limit of wind power output; The expression of the combined cooling, heating, electricity and hydrogen output constraint is:

[0061] In the formula, Pmin HP , Pmin AC , Pmin GT , Pmin HFC , Pmin EL , Pmin GB They are the lower limits of input power for heat pumps, electric refrigerators, gas turbines, hydrogen fuel cells, electrolyzers, and gas boilers. Pmax HP , Pmax AC , Pmax GT , Pmax HFC , Pmax EL , Pmax GB They are the upper limits of input power for heat pumps, electric refrigerators, gas turbines, hydrogen fuel cells, electrolyzers, and gas boilers; The expression of the energy storage device constraint is:

[0062]

[0063] In the formula, Smin H, Smax H, S H （t） are the lower and upper limits of the heat storage capacity of the heat storage tank and the heat storage state of the heat storage tank at time t, respectively. Smin B, Smax B, S B （t） are the lower and upper limits of the battery energy storage capacity and the energy storage state of the battery at time t, respectively. Smin HS, Smax HS, S HS （t） are the lower and upper limits of the heat storage capacity of the hydrogen storage tank and the hydrogen storage state of the hydrogen storage tank at time t, respectively. Pmax H, Pmax B, Pmax HS They are the maximum charging and discharging power of the heat storage tank, battery, and hydrogen storage tank, respectively. μ H , μ B , μ HS It is a 0-1 variable, which means that the energy storage device cannot be charged or discharged at the same time. When the value is 0, it means that the energy storage device is discharging energy, and when the value is 1, it means that the energy storage device is charging. The expression of the purchase and sale of electricity / gas constraints is:

[0064] In the formula, Pmax grid , Pmax gas The power limits for interacting with the power grid and gas grid are , μ grid It is a 0-1 variable, indicating that the purchase and sale of electricity cannot be restricted at the same time. When the value is 0, it means selling electricity to the grid, and when the value is 1, it means purchasing electricity from the grid.

[0065] In a specific embodiment of the present invention, the second objective function is solved based on the historical scheduling data and the constraint conditions to obtain the optimal scheduling plan for the day ahead under the minimum cost of the integrated energy system. Specifically, the following is obtained through the day ahead optimization scheduling model: Figure 8 , Fig. 9 , Fig.10 Hourly level scheduling plan for each equipment within the day shown.

[0066] In a specific embodiment of the present invention, intraday optimization scheduling is a hierarchical rolling optimization using model predictive control based on the day-ahead optimization scheduling results of the intraday optimization scheduling model, tracking and correcting the day-ahead plan to achieve coordinated operation of the system. The purpose of intraday optimization scheduling is to minimize the optimization deviation while ensuring the minimum system operating cost. The model is built through the MPC algorithm to track the scheduling data of the day when the actual operating status is known and correct the day-ahead scheduling data. The specific optimization scheduling model is as follows: Prediction model: The prediction model predicts the output value of the system at each moment in the future based on the control input and historical status information of the system at the current moment. The control input includes interaction with the power grid, power consumption of heat pumps and electric refrigerators, output power of gas turbines and hydrogen fuel cells, power consumption of electrolyzers, heating power of heat pumps, cooling power of electric refrigerators, hydrogen power consumption of hydrogen fuel cells, gas power consumption of gas turbines and gas boilers, and energy storage status of batteries, heat storage tanks, cold storage pipes and hydrogen storage tanks. Historical status information includes electric load data, heat load data, cold load data, electricity prices, heat prices, cold prices, natural gas prices, wind power data, and photovoltaic data. The output value includes the energy level of different energy storage devices, the working status of different devices, and the actual output power of different devices. It is mainly determined based on the error between the actual state and the reference predicted state. The state space model is established by optimizing the predicted output of the control system to continuously approach the reference predicted output. In some embodiments of the present invention, the expression of the state space prediction model is:

[0067] In the formula, X(k) represents the state variable of the integrated energy system, which refers to the energy level of the energy storage device at the current moment and the working status of different devices; Y(k) represents the output variable of the integrated energy system, which refers to the energy level of the energy storage device and the actual load provided; u(k) represents the input variable of the integrated energy system, which refers to various loads, electricity price, cooling price, heating price, and natural gas price; d(k) represents the disturbance variable of the integrated energy system, which refers to the change in the utilization power of renewable energy; A, B, C, and D are the matrices of the state space prediction model; The prediction model contains two important parameters: prediction time domain and control time domain. The prediction time domain refers to the time range step of the prediction model to predict the future, and the control time domain refers to the width of the system control range. The prediction time domain is proportional to the time range to which the prediction result belongs. The larger the prediction time domain, the more global the information prediction output is, but the trajectory tracking accuracy will decrease; the increase in the control time domain means a larger range of system adjustment and better control performance, but it will increase the calculation difficulty of the system. Here, the prediction time domain is selected as 4h and the control time domain is selected as 15min.

[0068] Rolling optimization: MPC uses a continuously rolling forward optimization method to solve, and obtains the optimal state of the performance indicators at the current moment to determine the future control state. Specifically, the electric load, cooling load, heating load and scheduling results of different equipment in the prediction time domain are obtained, and the control actions in the control time domain are executed, and the prediction time domain is pushed back by the corresponding time interval. The optimization result only acts on the control time domain, and the prediction cycle is continuously updated as time goes by. The minimum error between the actual state and the reference prediction state is taken as the optimization goal. The expression of the time domain rolling optimization model is:

[0069] In the formula, For the prediction time domain, for Always +The predicted value at time i, is the time t + Reference value at time i, w out is the output bias weight coefficient, w con is the control weight coefficient.

[0070] In the intraday rolling optimization stage, the Elastic regression prediction algorithm based on LASSO is used, and the multicollinearity problem is dealt with by introducing additional ridge regression penalty terms, thereby improving the stability and prediction performance of the model. The prediction model is used to predict the scheduling plan at the next moment based on the intraday scheduling plan and wind and solar processing. The Elastic Net loss function is:

[0071] in, is the L2 norm, which represents the sum of squares of all elements of the matrix. λ is the penalty parameter, λ1 is the first norm penalty, and λ2 is the second norm penalty. Both the L1 norm and the L2 norm have the function of compressing the regression coefficients. Using the L1 norm and the L2 norm to achieve double compression will lead to larger estimation deviations, so further improvements are needed. Let μ=λ1 / (λ1+λ2), λ=λ1+λ2, then:

[0072] Among them, μ is the weight factor of L1 norm and L2 norm. When λ2=0, the penalty is L1 norm, which is LASSO regression. Its sparsity can automatically select eigenvalues, reduce model complexity, improve the generalization ability of the model, and avoid overfitting. When λ1=0, the penalty term is L2 norm, which is ridge regression. The L2 penalty term added by ridge regression makes the regression coefficient more realistic and reasonably avoids the adverse effects of multicollinearity on the model.

[0073] The prediction model is solved by calculating the size of θ when the loss function is minimum, where θ=argminθJ(θ). Due to the existence of absolute values ​​in the loss function, the analytical solution of θ cannot be obtained by direct derivation. Therefore, the gradient descent method is used to minimize the loss function, improve the fitting degree of regression prediction, and obtain the optimal prediction model. g(θTX) represents the final objective function of regression:

[0074] In the intraday rolling optimization, the X independent variables are load, wind and solar energy, equipment output, and time-of-use price, and the load and Y dependent variables are the SOC of the energy storage equipment. Through the regression prediction algorithm, the intraday stage t to t0+k The historical data at each time point is used as the training set for training. The trained model is used according to the day-ahead dispatch plan, the real-time updated photovoltaic and wind power output, and a Ultra-short-term forecast information for a period of time, predict the operating status at the next moment, and obtain multiple variable information of the system.

[0075] According to the loss function, the data-driven optimization prediction loss function based on regression prediction is obtained as follows:

[0076] in, , , are the state quantity, predicted input quantity and interference quantity at time k respectively, is the initial state quantity.

[0077] Initialize the algorithm-related parameters, and initialize θi, the algorithm termination distance ε and the step length α according to the day-ahead scheduling plan and system status at time t0. The step length α is multiplied by the gradient of the loss function descent to obtain the distance of the current position descent. If it is less than ε, the optimization result is output. Otherwise, θi is updated again. Based on the above method of calculating the descent gradient, the model for solving the minimum loss function is:

[0078] Among them, Q is the optimization parameter, Z is the gradient descent direction, Ns, Np, Nd are the optimization times of the initial state, predicted input value and interference respectively, and ▽Z is the descent gradient.

[0079] Figure 7 A schematic diagram of model predictive control rolling optimization is shown in an embodiment of a multi-time scale optimization scheduling method for an integrated energy system provided by the present invention.

[0080] Feedback correction: In the actual predictive control process, due to the interference of various uncertain factors, the solution results will be different from the actual state value, so dynamic feedback adjustment is required within a small time scale. The system executes the control plan of the current time step, re-predicts in the next time step, and compensates for the latest error in the subsequent optimization process. That is, after executing the scheduling plan in the control time domain, the error between the intraday scheduling plan and the day-ahead scheduling plan is statistically analyzed to minimize the error. The above process constitutes a closed-loop optimization, making the optimization strategy more effective.

[0081] In some embodiments of the present invention, the correction formula for correcting the state of the integrated energy system according to the intraday scheduling plan, that is, the feedback correction formula is:

[0082] In the formula, is the error between the predicted value and the actual value, is the error coefficient matrix.

[0083] It should be noted that the purpose of intraday optimization scheduling is to minimize the system operation cost while minimizing the optimization deviation. In some embodiments of the present invention, the expression of the first objective function is:

[0084] Where Npre is the prediction time domain, M(t+i|t) is the power and storage status of each device at time t to time t+i, and N(t+i|t) is the reference value of the power and storage status of each device at time t to time t+i.

[0085] In a specific embodiment of the present invention, the following is obtained by optimizing the scheduling model within a day: Fig.11 , Fig.12 , Fig.13 The 15-minute level scheduling plan for each device within the day is shown.

[0086] This embodiment converts thermal energy, hydrogen energy, cold energy, and natural gas into a unified unit of measurement, measures the power of various energy sources in kW, linearly models each device in the grid, converts the multi-time scale optimization scheduling problem of the wind, solar, and hydrogen integrated energy system into a mixed integer linear programming problem, and uses the YALMIP modeling tool to call the CPLEX solver to complete the global optimal solution on the MATLAB platform.

[0087] In order to better implement a multi-time scale optimization scheduling method for an integrated energy system in an embodiment of the present invention, based on a multi-time scale optimization scheduling method for an integrated energy system, correspondingly, Fig.14 As shown, an embodiment of the present invention further provides a multi-time scale optimization scheduling device for an integrated energy system. A multi-time scale optimization scheduling device 1400 for an integrated energy system includes: The day-ahead dispatch plan acquisition module 1401 is used to acquire and obtain the day-ahead optimal dispatch plan under the minimum cost of the integrated energy system based on the load historical dispatch data and the response load of the integrated energy system; A model building module 1402 is used to build a state space prediction model and an intraday scheduling model of the integrated energy system, wherein the intraday scheduling model includes a first objective function and constraint conditions; The intraday dispatch plan acquisition module 1403 is used to construct and solve the time domain rolling optimization model based on the state space prediction model according to the optimal dispatch plan of the day, the constraint conditions and the first objective function, so as to obtain the intraday dispatch plan of the load; The revised intraday dispatch plan acquisition module 1405 is used to correct the state of the integrated energy system according to the intraday dispatch plan to obtain a revised intraday dispatch plan of the load.

[0088] The multi-time-scale optimization and scheduling device 1400 for an integrated energy system provided in the above embodiment can implement the technical solution described in the above embodiment of a multi-time-scale optimization and scheduling method for an integrated energy system. The specific implementation principles of the above modules or units can refer to the corresponding contents in the above embodiment of a multi-time-scale optimization and scheduling method for an integrated energy system, which will not be repeated here.

[0089] like Fig.15 As shown, the present invention also provides an electronic device 1500. The electronic device 1500 includes a processor 1501, a memory 1502 and a display 1503. Fig.15Only some components of the electronic device 1500 are shown, but it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0090] In some embodiments, processor 1501 may be a central processing unit (CPU), a microprocessor or other data processing chip, used to run program codes or process data stored in memory 302, such as a multi-time scale optimization scheduling method for an integrated energy system in the present invention.

[0091] In some embodiments, processor 1501 may be a single server or a server group. The server group may be centralized or distributed. In some embodiments, processor 1501 may be local or remote. In some embodiments, processor 1501 may be implemented in a cloud platform. In some embodiments, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-cloud, etc., or any combination thereof.

[0092] In some embodiments, the memory 1502 may be an internal storage unit of the electronic device 1500, such as a hard disk or memory of the electronic device 1500. In other embodiments, the memory 1502 may also be an external storage device of the electronic device 1500, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the electronic device 1500.

[0093] Furthermore, the memory 1502 may include both an internal storage unit of the electronic device 1500 and an external storage device. The memory 1502 is used to store application software installed in the electronic device 1500 and various data.

[0094] In some embodiments, the display 1503 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, etc. The display 1503 is used to display information on the electronic device 1500 and to display a visual user interface. The components 1501-1503 of the electronic device 1500 communicate with each other via a system bus.

[0095] In one embodiment, when the processor 1501 executes a multi-time-scale optimization scheduling program for an integrated energy system in the memory 1502, the following steps may be implemented: Obtain and obtain the optimal day-ahead dispatch plan of the integrated energy system at the minimum cost based on the historical dispatch data and response load of the integrated energy system; Construct a state space prediction model and an intraday scheduling model for the integrated energy system. The intraday scheduling model includes the first objective function and constraints. According to the optimal dispatch plan of the day ahead, the constraints and the first objective function, a time domain rolling optimization model is constructed and solved based on the state space prediction model to obtain the intraday dispatch plan of the load; The state of the integrated energy system is corrected according to the intraday dispatch plan to obtain a corrected load intraday dispatch plan.

[0096] It should be understood that: when the processor 1501 executes a multi-time scale optimization scheduling program for an integrated energy system in the memory 1502, in addition to the above functions, other functions can also be implemented. For details, please refer to the description of the corresponding method embodiment above.

[0097] Furthermore, the embodiment of the present invention does not specifically limit the type of the electronic device 1500 mentioned, and the electronic device 1500 may be a portable electronic device such as a mobile phone, a tablet computer, a personal digital assistant (PDA), a wearable device, a laptop computer, etc. Exemplary embodiments of portable electronic devices include but are not limited to portable electronic devices equipped with IOS, Android, Microsoft or other operating systems. The above-mentioned portable electronic devices may also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the present invention, the electronic device 1500 may not be a portable electronic device, but a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0098] Those skilled in the art will appreciate that all or part of the processes of the above-mentioned embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, wherein the computer-readable storage medium is a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0099] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A multi-time scale optimization scheduling method for an integrated energy system, characterized in that: include: Obtain and obtain the optimal day-ahead dispatch plan of the integrated energy system at the minimum cost based on the historical dispatch data and response load of the integrated energy system; Construct a state space prediction model and an intraday scheduling model for the integrated energy system. The intraday scheduling model includes the first objective function and constraints. According to the optimal dispatch plan of the day ahead, the constraints and the first objective function, a time domain rolling optimization model is constructed and solved based on the state space prediction model to obtain the intraday dispatch plan of the load; The state of the integrated energy system is corrected according to the intraday dispatch plan to obtain a corrected load intraday dispatch plan.

2. The multi-time scale optimization scheduling method for an integrated energy system according to claim 1 is characterized in that: The step of obtaining and obtaining the optimal day-ahead dispatching plan of the integrated energy system at the minimum cost based on the historical dispatching data of the load and the response load of the integrated energy system includes: Acquire historical load dispatch data and response load of the integrated energy system, wherein the response load includes heat load, cooling load and electric load; Building a day-ahead dispatching model based on the response load of the integrated energy system, wherein the day-ahead dispatching model includes a second objective function and constraint conditions; The second objective function is solved based on the load historical dispatch data and constraint conditions to obtain the day-ahead optimal dispatch plan at the minimum cost of the integrated energy system.

3. The multi-time scale optimization scheduling method for the integrated energy system according to claim 2 is characterized in that: Obtain the response load of the integrated energy system, including: Establish a price elasticity matrix of comprehensive demand response adjustment coefficients based on price elasticity for the integrated energy system; The response load of the integrated energy system is obtained based on the price elasticity matrix.

4. The multi-time scale optimization scheduling method for an integrated energy system according to claim 2, characterized in that: The expression of the second objective function is: In the formula, F 1 is the total operating cost of the wind, solar and hydrogen integrated energy system; F grid (t) Indicates the cost of electricity purchase and sale for the entire system; F op (t) The operation and maintenance cost of the entire system; F gas (t) is the fuel cost of the entire system; F buy (t) is the electricity purchase cost, F sell (t) F is the income from electricity sales; PV (t) is the operation and maintenance cost of photovoltaic equipment, F WT (t) is the operation and maintenance cost of wind power equipment, F HP (t) is the operation and maintenance cost of heat pump equipment, F GT (t) The operation and maintenance cost of gas turbine equipment is F GB (t) For gas boiler equipment operation and maintenance costs, F AC (t) is the operation and maintenance cost of the electric refrigerator equipment, F H (t) is the operation and maintenance cost of thermal energy storage equipment, F B (t) is the battery equipment operation and maintenance cost, F HS (t) is the operation and maintenance cost of hydrogen energy storage equipment, F EL (t) is the operation and maintenance cost of the electrolyzer equipment, F HFC (t) Operation and maintenance costs of hydrogen fuel cell equipment; F GT,gas (t) is the gas turbine fuel cost, F GB,gas (t) Fuel cost for gas boiler.

5. The multi-time scale optimization scheduling method for an integrated energy system according to claim 1, characterized in that: The constraints include energy supply and demand balance constraints, equipment operation constraints, and electricity / gas purchase and sale constraints: The energy supply and demand balance constraints include: electric energy balance constraints, heat balance constraints, cooling balance constraints, hydrogen balance constraints, and natural gas balance constraints; The equipment operation constraints include: photovoltaic and wind power output constraints, combined cooling, heating, electricity and hydrogen output constraints, and energy storage equipment constraints; The expression of the power balance constraint is: In the formula, P load,e (t) for t The electrical load power at the moment, P buy,e (t) , P sell,e (t) are t Purchase and sell electricity at all times; P WT (t) , P PV (t) are the photovoltaic and wind power outputs at time t, P HP,e (t) , P AC,e (t) They are t The power consumption of heat pumps and electric refrigerators at all times, P GT (t) , P HFC (t) They are t The output power of gas turbines and hydrogen fuel cells at all times, P EL,con (t) for t The power consumption of the electrolytic cell at all times; The expression of the heat balance constraint is: In the formula, P load,h (t) for t Heat load power at the moment, P HP (t) is the heating power of the heat pump at time t; The expression of the cooling balance constraint is: In the formula, P load,c (t ) is the cooling load power at time t, P AC (t) is the cooling power of the electric refrigerator at time t; The expression of the hydrogen balance constraint is: In the formula, P EL (t) for t The hydrogen production power of the electrolyzer at the moment, P HFC,H 2(t) for t The hydrogen power consumption of hydrogen fuel cells at all times; The expression of the natural gas balance constraint is: In the formula, P buy,gas (t) is the gas purchasing power at time t, P GT,gas (t) is the gas consumption power of the gas turbine at time t, P GB,gas (t) is the gas consumption power of the gas boiler at time t; The expression of the photovoltaic and wind power output constraints is: In the formula, Pmax PV The upper limit of photovoltaic power generation output , Pmax WT The upper limit of wind power output; The expression of the combined cooling, heating, electricity and hydrogen output constraint is: In the formula, Pmin HP , Pmin AC , Pmin GT , Pmin HFC , Pmin EL , Pmin GB They are the lower limits of input power for heat pumps, electric refrigerators, gas turbines, hydrogen fuel cells, electrolyzers, and gas boilers. Pmax HP , Pmax AC , Pmax GT , Pmax HFC , Pmax EL , Pmax GB They are the upper limits of input power for heat pumps, electric refrigerators, gas turbines, hydrogen fuel cells, electrolyzers, and gas boilers; The expression of the energy storage device constraint is: In the formula, Smin H, Smax H, S H （t） are the lower and upper limits of the heat storage capacity of the heat storage tank and the heat storage state of the heat storage tank at time t, respectively. Smin B, Smax B, S B （t） are the lower and upper limits of the battery energy storage capacity and the energy storage state of the battery at time t, respectively. Smin HS, Smax HS, S HS （t） are the lower and upper limits of the heat storage capacity of the hydrogen storage tank and the hydrogen storage state of the hydrogen storage tank at time t, respectively. Pmax H, Pmax B, Pmax HS They are the maximum charging and discharging power of the heat storage tank, battery, and hydrogen storage tank, respectively. μ H , μ B , μ HS It is a 0-1 variable, which means that the energy storage device cannot be charged or discharged at the same time. When the value is 0, it means that the energy storage device is discharging energy, and when the value is 1, it means that the energy storage device is charging. The expression of the purchase and sale of electricity / gas constraints is: In the formula, Pmax grid , Pmax gas The power limits for interacting with the power grid and gas grid are , μ grid It is a 0-1 variable, indicating that the purchase and sale of electricity cannot be restricted at the same time. When the value is 0, it means selling electricity to the grid, and when the value is 1, it means purchasing electricity from the grid.

6. The multi-time scale optimization scheduling method for the integrated energy system according to claim 4 is characterized in that: The expression of the first objective function is: In the formula, For the prediction time domain, for Always The power and storage status of each device at any time. for Always Reference values ​​for the power and storage status of each device at a given time and day.

7. The multi-time scale optimization scheduling method for an integrated energy system according to claim 1, characterized in that: The expression of the state space prediction model is: In the formula, X(k) represents the state variable of the integrated energy system, which refers to the energy level of the energy storage device at the current moment and the working status of different devices; Y(k) represents the output variable of the integrated energy system, which refers to the energy level of the energy storage device and the actual load provided; u(k) represents the input variable of the integrated energy system, which refers to various loads, electricity price, cooling price, heating price, and natural gas price; d(k) represents the disturbance variable of the integrated energy system, which refers to the change in the utilization power of renewable energy; A, B, C, and D are the matrices of the state space prediction model; The expression of the time domain rolling optimization model is: In the formula, For the prediction time domain, for Always +The predicted value at time i, is the time t + Reference value at time i, is the output bias weight coefficient, is the control weight coefficient; The correction formula for correcting the state of the integrated energy system according to the intraday dispatch plan is: In the formula, is the error between the predicted value and the actual value, is the error coefficient matrix.

8. A multi-time scale optimization scheduling device for an integrated energy system, characterized in that: include: The day-ahead dispatch plan acquisition module is used to acquire and obtain the day-ahead optimal dispatch plan of the integrated energy system at the minimum cost based on the load historical dispatch data and response load of the integrated energy system; A model building module is used to build a state space prediction model and an intraday scheduling model for the integrated energy system. The intraday scheduling model includes a first objective function and constraints. The intraday dispatch plan acquisition module is used to construct and solve the time domain rolling optimization model based on the state space prediction model according to the optimal dispatch plan of the day, the constraints and the first objective function, so as to obtain the intraday dispatch plan of the load; The revised intraday dispatch plan acquisition module is used to correct the state of the integrated energy system according to the intraday dispatch plan to obtain a revised intraday dispatch plan for the load.

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

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

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