Power scheduling method and device for multi-energy distributed system and electronic equipment

Abstract

The invention discloses a power scheduling method and device for a multi-energy distributed system and electronic equipment. The method comprises the following steps: acquiring system parameters of the multi-energy distributed system; according to the system parameters, an objective function with the lowest system operation cost as an objective is determined, the objective function comprises a convertible supply load compensation cost item, a convertible supply load is a convertible load supplied in other energy forms, and conversion at least comprises conversion between electric energy and heat energy; determining a constraint condition corresponding to the target function; and solving the target function under the constraint condition by using a solver to obtain a scheduling power parameter of the multi-energy distributed system, and performing power scheduling according to the scheduling power parameter. According to the method and the device, the technical problem that the capacity of the equipment is difficult to fully utilize due to the fact that the energy supply condition of the equipment is mostly considered when the power strategy of the multi-energy distributed system is formulated in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the field of electric power data, and in particular to a power scheduling method, device and electronic equipment for a multi-energy distributed system. Background Art

[0002] The installed capacity of new energy sources such as wind turbines and photovoltaics is increasing year by year. In order to balance social development and environmental protection, it is urgent to seek optimization of energy structure and efficient use of energy. The coordinated and sustainable development of multiple energy systems is an inevitable trend in the development of comprehensive energy systems in the future. Therefore, the energy interconnection system has broad development prospects.

[0003] The integrated energy system involves multiple disciplines, including electricity, heat, gas and other energy forms. When formulating the power strategy of the multi-energy distributed system, the energy supply of the equipment itself is often considered, which leads to the technical problem of difficulty in fully utilizing the equipment capacity.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] The embodiments of the present invention provide a power scheduling method, device and electronic device for a multi-energy distributed system, so as to at least solve the technical problem in the related art that when formulating the power strategy of the multi-energy distributed system, more consideration is given to the energy supply situation of the device itself, and it is difficult to fully utilize the device capacity.

[0006] According to one aspect of an embodiment of the present invention, a power scheduling method for a multi-energy distributed system is provided, comprising: obtaining system parameters of the multi-energy distributed system, wherein the system parameters include electric load parameters, thermal load parameters and cooling load parameters; determining an objective function with the goal of minimizing system operation costs based on the system parameters, wherein the objective function includes a shiftable load compensation cost item, a transferable load compensation cost item, a reducible load compensation cost item and a convertible supply load compensation cost item, wherein the convertible supply load is a load that can be converted and supplied by other energy forms, and the conversion includes at least the conversion between electric energy and thermal energy; determining constraints corresponding to the objective function, wherein the constraints include shiftable load constraints, transferable load constraints, reducible load constraints, convertible supply energy constraints and integrated energy system power balance constraints; using a solver to solve the objective function under the constraints to obtain scheduling power parameters of the multi-energy distributed system, so as to perform power scheduling based on the scheduling power parameters.

[0007] Optionally, before determining the constraints corresponding to the objective function, it also includes: determining the translation time corresponding to the transferable load; determining the transfer time corresponding to the transferable load, the transferable power corresponding to the transferable load at the transfer time, and the reserved power corresponding to the transferable load; determining the reducible time, the reduction duration and the number of reductions corresponding to the reducible load; constructing the transferable load compensation cost item and the transferable load constraint based on the translation time, and constructing the transferable load compensation cost item and the transferable load constraint based on the transfer time, the transferable power and the reserved power, and constructing the reducible load compensation cost item and the reducible load constraint based on the reducible time, the reduction duration and the number of reductions.

[0008] Optionally, determining the reducible time, reduction duration and number of reductions corresponding to the reducible load includes: determining the reducible time corresponding to the reducible load; determining a reduction coefficient corresponding to the reducible time, wherein the reduction coefficient is used to indicate the degree of reduction in user experience after load reduction; and determining, based on the reduction coefficient, the reduction duration and number of reductions in different time periods within the reducible time.

[0009] Optionally, before determining the constraints corresponding to the objective function, it also includes: determining a conversion relationship corresponding to an energy conversion device, wherein the energy conversion device includes a device for converting between a first energy and a second energy; when a first energy surplus is generated in a predetermined time period, determining whether there is a demand result for a second energy demand in the predetermined time period; when the demand result is that there is a demand for the second energy, determining the first energy conversion amount in the time period based on the first energy surplus and the second energy demand; and constructing the convertible supply energy constraint and the convertible supply load compensation cost item based on the first energy conversion amount in the time period.

[0010] Optionally, before determining the constraint conditions corresponding to the objective function, it also includes: determining the load equipment in the multi-energy distributed system, wherein the load equipment includes: rigid thermal load, shiftable thermal load, reducible thermal load, rigid cooling load, shiftable cooling load, and reducible cooling load; the thermal load parameters include a first thermal load parameter corresponding to the rigid thermal load, a second thermal load parameter corresponding to the shiftable thermal load, and a third thermal load parameter corresponding to the reducible thermal load; the cooling load parameters include a first cooling load parameter corresponding to the rigid cooling load, a second cooling load parameter corresponding to the shiftable cooling load, and a third cooling load parameter corresponding to the reducible cooling load; based on the first thermal load parameter, the second thermal load parameter, and the third thermal load parameter, a thermal power balance constraint is determined; based on the first cooling load parameter, the second cooling load parameter, and the third cooling load parameter, a cooling power balance constraint is determined; and based on the electrical load parameters, an electrical power balance constraint is determined; based on the thermal power balance constraint, the cooling power balance constraint, and the electrical power balance constraint, the power balance constraint of the integrated energy system is determined.

[0011] Optionally, based on the system parameters, an objective function with the goal of minimizing the system operating cost is determined, including: determining a pre-power scheduling parameter; based on the pre-power scheduling parameter, determining whether there is a transfer result of a detachable device in the multi-energy distributed system; when the transfer result is that there is a detachable device, determining whether there is a demand result for energy demand corresponding to the detachable device in the short-distance power grid system, wherein the short-distance power grid system is a system within a predetermined range from the multi-energy distributed system; when the demand result is that there is an energy demand corresponding to the detachable device, the detachable device is transferred to the short-distance power grid system for use by the short-distance power grid system.

[0012] Optionally, after using a solver to solve the objective function under the constraints and obtaining the scheduling power parameters of the multi-energy distributed system, it also includes: controlling the multi-energy distributed system to perform power scheduling based on the scheduling power parameters; determining a system feedback index after power scheduling operation with the scheduling power parameters; and adjusting the scheduling power parameters based on the system feedback index to obtain updated power parameters.

[0013] According to one aspect of an embodiment of the present invention, a power dispatching device for a multi-energy distributed system is provided, comprising: an acquisition module, used to acquire system parameters of the multi-energy distributed system, wherein the system parameters include electric load parameters, thermal load parameters and cooling load parameters; a first determination module, used to determine an objective function with the goal of minimizing system operation costs based on the system parameters, wherein the objective function includes a shiftable load compensation cost item, a transferable load compensation cost item, a reducible load compensation cost item and a convertible supply load compensation cost item, wherein the convertible supply load is a load that can be converted and supplied by other energy forms, and the conversion at least includes the conversion between electric energy and thermal energy; a second determination module, used to determine the constraints corresponding to the objective function, wherein the constraints include shiftable load constraints, transferable load constraints, reducible load constraints, convertible supply energy constraints and integrated energy system power balance constraints; a third determination module, used to use a solver to solve the objective function under the constraints to obtain the dispatching power parameters of the multi-energy distributed system, so as to perform power dispatch based on the dispatching power parameters.

[0014] According to one aspect of an embodiment of the present invention, there is provided an electronic device, comprising: a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured to execute the instructions to implement a power scheduling method for a multi-energy distributed system as described in any one of the above.

[0015] According to one aspect of an embodiment of the present invention, a computer-readable storage medium is provided. When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute any of the power scheduling methods for a multi-energy distributed system described above.

[0016] In an embodiment of the present invention, system parameters of a multi-energy distributed system are obtained, wherein the system parameters include electric load parameters, heat load parameters, and cooling load parameters. Based on the system parameters, an objective function with the lowest system operating cost as the goal is determined, wherein the objective function includes a shiftable load compensation cost item, a transferable load compensation cost item, a reducible load compensation cost, a convertible supply load compensation cost item, and the convertible supply load is a load that can be converted to a load supplied by other energy forms, and the conversion includes at least the conversion between electric energy and thermal energy. The constraints corresponding to the objective function are determined, wherein the constraints include shiftable load constraints, transferable load constraints, reducible load constraints, convertible supply energy constraints, and integrated energy system power balance constraints. A solver is used to solve the objective function under the constraints to obtain the dispatching power parameters of the multi-energy distributed system, so as to perform power dispatch according to the dispatching power parameters. In the embodiment of the present invention, not only the cold load, the heat load and the electric load are taken into consideration, but also the shiftable, transferable and reducible loads, and even the convertible supply energy. It can be seen that when determining the power strategy of a multi-energy distributed system, in addition to considering the energy supply situation of the equipment itself, the situations of being able to be dispatched, reduced and converted are also considered, so that the power strategy determined by the multi-energy distributed system is more intelligent and more efficient, thereby solving the technical problem in the related technology that when formulating the power strategy of a multi-energy distributed system, the energy supply situation of the equipment itself is mostly considered, and it is difficult to fully utilize the equipment capacity. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0018] Figure 1 is a flow chart of a power dispatching method for a multi-energy distributed system according to an embodiment of the present invention;

[0019] Figure 2 A flow chart for solving a multi-energy complementary day-ahead optimal operation and dispatching model for a regional integrated energy system provided in an optional embodiment of the present invention;

[0020] Figure 3 A schematic diagram of wind turbine and photovoltaic prediction data of a regional integrated energy system multi-energy complementary day-ahead optimization operation scheduling model provided by an optional implementation mode of the present invention;

[0021] Figure 4 A schematic diagram of electric load forecasting data of a multi-energy complementary day-ahead optimization operation scheduling model for a regional integrated energy system provided in an optional embodiment of the present invention;

[0022] Figure 5A schematic diagram of heat load forecasting data of a multi-energy complementary day-ahead optimization operation scheduling model for a regional integrated energy system provided in an optional embodiment of the present invention;

[0023] Figure 6 A schematic diagram of cold load forecast data of a multi-energy complementary day-ahead optimization operation scheduling model for a regional integrated energy system provided in an optional embodiment of the present invention;

[0024] Figure 7 A schematic diagram of the time-of-use electricity price of a multi-energy complementary day-ahead optimization operation scheduling model for a regional integrated energy system provided in an optional implementation manner of the present invention;

[0025] Figure 8 A schematic diagram of the output of each device of the power load supplied by the multi-energy complementary day-ahead optimization operation scheduling model of the regional integrated energy system provided by an optional implementation mode of the present invention;

[0026] Fig. 9 A schematic diagram of the output of each device of the thermal load supplied by the multi-energy complementary day-ahead optimization operation scheduling model of the regional integrated energy system provided by an optional implementation mode of the present invention;

[0027] Fig.10 A schematic diagram of the output of each device supplying cooling load in a multi-energy complementary day-ahead optimization operation scheduling model for a regional integrated energy system provided in an optional implementation manner of the present invention;

[0028] Fig.11 A schematic diagram of a shiftable electric load of a regional integrated energy system multi-energy complementary day-ahead optimization operation scheduling model provided in an optional implementation mode of the present invention;

[0029] Fig.12 A schematic diagram of transferable electric loads of a regional integrated energy system multi-energy complementary day-ahead optimization operation scheduling model provided in an optional implementation mode of the present invention;

[0030] Fig.13 A schematic diagram of the electric load that can be reduced in a regional integrated energy system multi-energy complementary day-ahead optimization operation scheduling model provided in an optional implementation mode of the present invention;

[0031] Fig.14 A schematic diagram of the total electric load change of a multi-energy complementary day-ahead optimization operation scheduling model for a regional integrated energy system provided in an optional implementation manner of the present invention;

[0032] Fig.15 It is a structural block diagram of a power dispatching device for a multi-energy distributed system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0033] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0034] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0035] First, the terms involved in the present invention are introduced:

[0036] Matlab: Mathematical software, mainly used in data analysis, wireless communications, deep learning, image processing and computer vision, signal processing, quantitative finance and risk management, robotics, control systems and other fields. Matlab is a combination of the two words matrix & laboratory, which means matrix factory (matrix laboratory). It integrates many powerful functions such as numerical analysis, matrix calculation, scientific data visualization, and modeling and simulation of nonlinear dynamic systems in an easy-to-use window environment, providing a comprehensive solution for scientific research, engineering design, and many scientific fields that require effective numerical calculations.

[0037] Yalmip: is an open source toolbox based on Matlab for setting up and solving convex optimization problems. It provides a simple and powerful way to describe and solve linear, quadratic and quadratically constrained optimization problems, while supporting mixed integer programming problems, non-convex optimization problems and semidefinite programming problems.

[0038] Cplex: A high-performance mathematical optimization solver used to solve various linear programming, nonlinear programming, integer programming and other models. It provides a calling interface in multiple languages ​​to facilitate user integration and use in different environments.

[0039] Example 1

[0040] According to an embodiment of the present invention, an embodiment of a power scheduling method for a multi-energy distributed system is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0041] Figure 1 is a flow chart of a power dispatching method for a multi-energy distributed system according to an embodiment of the present invention. Figure 1 As shown, the method comprises the following steps:

[0042] Step S102, obtaining system parameters of the multi-energy distributed system, wherein the system parameters include electric load parameters, heat load parameters, and cooling load parameters;

[0043] In step S102 provided in the present application, the system parameters of the multi-energy distributed system are obtained, wherein the multi-energy distributed system is involved. The multi-energy distributed system can integrate multiple energy conversion and storage technologies (such as solar energy, wind energy, geothermal energy, gas turbines, etc.), as well as the distribution and utilization of multiple energy forms such as electrical energy, thermal energy, and cold energy. They aim to improve energy utilization efficiency, reduce dependence on traditional energy, and promote the widespread application of renewable energy.

[0044] Among them, the system parameters of the multi-energy distributed system are also involved. The system parameters are key data or indicators that describe the operating status and performance characteristics of the system. Among them, it can include electric load parameters, which refer to the power demand information borne by the system, including but not limited to the total electric load, peak-valley load difference, load rate, load forecast, etc. Understanding the electric load parameters helps to reasonably arrange power production and distribution, reduce the pressure on the power grid, and improve the reliability and economy of power supply. Among them, it can also include thermal load parameters, which refer to the total amount of thermal energy and related characteristic parameters required by the system to meet the needs of users such as heating and hot water. The analysis of thermal load parameters helps to optimize the thermal energy production and distribution strategy, improve energy utilization efficiency, and reduce energy waste. Among them, it also involves cold load parameters. Corresponding to the thermal load, the cold load parameters reflect the cold capacity and characteristics required by the system to meet the cooling needs of users. It is particularly important in the fields of air-conditioning systems and refrigerated storage. Accurately grasping the cold load parameters helps to achieve precise temperature control, improve cooling efficiency, and reduce energy consumption. By accurately grasping various load parameters, the system can dispatch and manage energy more intelligently, avoid energy waste, and maximize energy utilization.

[0045] It should be noted that system parameters may include, but are not limited to, data related to electrical load, thermal load, and cooling load, and may also include data such as energy prices, conversion efficiency, and energy storage status.

[0046] Step S104, determining an objective function with the lowest system operation cost as the goal based on the system parameters, wherein the objective function includes a shiftable load compensation cost item, a transferable load compensation cost item, a reducible load compensation cost item, a convertible supply load compensation cost item, and a convertible supply load is a load that can be converted to be supplied by other energy forms, and the conversion at least includes the conversion between electric energy and thermal energy;

[0047] In step S104 provided in the present application, an objective function with the goal of minimizing the system operating cost is determined based on the system parameters. The objective function here is to minimize the system operating cost and aims to maximize the economic benefits of the system by optimizing strategies such as energy distribution, load adjustment and energy conversion.

[0048] The objective function includes the item of shiftable load compensation cost, which refers to the compensation cost required to shift certain loads from high-cost periods to low-cost periods. For example, through intelligent scheduling, part of the power load is shifted from the peak period to the valley period, and the entire section is shifted.

[0049] The objective function includes a transferable load compensation cost item, which is similar to the shiftable load, but may involve a wider range of temporal and spatial transfers. For example, excess power in certain areas may be transferred to areas with power shortages, or loads may be redistributed in different time periods. In other words, it may be an incomplete transfer, where only a portion of the power may be transferred.

[0050] The objective function includes the item of compensation for load reduction, which refers to the compensation for users to reduce load during a specific period of time. This helps to relieve the pressure of the system during peak hours and reduce the peak load of the power grid.

[0051] Among them, the objective function includes the compensation cost item for convertible supply loads, especially for convertible supply loads, that is, those loads that can be converted from one energy form to another energy form to meet demand (such as the conversion between electrical energy and thermal energy). This conversion requires additional equipment and operating costs, and may also lead to improved energy utilization efficiency. Therefore, considering the compensation cost of convertible supply loads in the objective function helps to balance the economic benefits and costs brought about by the conversion.

[0052] By comprehensively considering a variety of load adjustment measures and their compensation costs, the objective function can more accurately evaluate the impact of different strategies on the system operating costs, thereby selecting the lowest cost operation plan. By introducing concepts such as shiftable, transferable, reducible, and convertible supply loads, the system's flexibility in dealing with load fluctuations and imbalances in energy supply and demand is increased. This helps improve the stability and reliability of the system.

[0053] Step S106, determining the constraint conditions corresponding to the objective function, wherein the constraints include a load constraint that can be translated, a load constraint that can be transferred, a load constraint that can be reduced, a load constraint that can be converted to supply energy, and a power balance constraint of the integrated energy system;

[0054] In step S106 provided in the present application, the constraint conditions corresponding to the objective function are determined.

[0055] Among them, there are shiftable load constraints, which limit the range and degree of load shifting in time. For example, some loads may only be shifted during a specific time period, or the total amount of shifting cannot exceed a certain limit. This is to ensure that load shifting does not have an adverse impact on other time periods or other parts of the system. Among them, time window constraints can also be included, that is, the time range limit of load shifting. This helps to avoid large load adjustments during peak periods, thereby reducing system pressure.

[0056] Among them, the transferable load constraint is involved, which is similar to the shiftable load constraint, but focuses more on the transfer of loads between spaces, which is an incomplete transfer. This constraint may involve multiple aspects such as power grid transmission capacity, inter-regional energy policies, and energy market rules. Among them, it can also include time window constraints, that is, the time range limit of load shifting.

[0057] Among them, load reduction constraints are involved: this constraint specifies the maximum limit and conditions for load reduction. For example, some critical loads may not be allowed to be reduced, or the reduction must meet specific priorities and sequences. In addition, load reduction may also be limited by factors such as user satisfaction and contractual agreements.

[0058] Among them, the convertible supply energy constraint is involved, which involves the conversion efficiency and capacity between different energy forms. For example, the conversion of electrical energy into thermal energy may be limited by factors such as equipment conversion efficiency, equipment capacity, and operating costs. In addition, additional energy losses or emissions may be generated during the conversion process, which also needs to be considered in the constraints.

[0059] Among them, the power balance constraint of the integrated energy system is involved. This is the most basic constraint condition, which requires that at any time, the total supply of the system must be equal to or greater than the total demand. This includes the balance of multiple energy forms such as electricity, heat, and cold energy. This constraint ensures the stable operation of the system and the reliability of power supply.

[0060] It should be noted that in addition to the above constraints, energy price constraints can also be included. Energy price is an important factor affecting the system operation cost. When constructing the objective function, energy price can be taken into account as a dynamic variable, and corresponding price constraints can be set to reflect the impact of market price fluctuations on the system operation cost. Environmental constraints can also be included, that is, in the integrated energy system, the impact of environmental factors also needs to be considered. For example, emission restrictions, renewable energy quotas, etc. can all be included as constraints in the objective function.

[0061] By considering various load adjustment measures and constraints of energy conversion methods, the objective function can more comprehensively evaluate the impact of different strategies on system costs and resource utilization efficiency, thereby guiding the system to shift to a more economical and efficient resource allocation method.

[0062] Step S108, using a solver to solve the objective function under the constraint conditions, obtain the dispatching power parameters of the multi-energy distributed system, and perform power dispatch according to the dispatching power parameters.

[0063] In step S108 provided in the present application, it is explained that a solver is used to solve the objective function under the constraints to obtain the dispatching power parameters of the multi-energy distributed system. Among them, a solver is used, and the optimal solution or the approximate optimal solution can be searched through a mathematical algorithm according to the given objective function and constraints. In a multi-energy distributed system, the solver can handle complex nonlinear optimization problems and quickly give the optimization results of the dispatching power parameters. The dispatching power parameters are determined, which refer to the power values ​​allocated at different time points and different energy units. These parameters are the concrete embodiment of the optimization results and are used to guide the actual power dispatching operations. By optimizing the dispatching power parameters, the system can allocate and utilize various energy resources more reasonably, thereby improving energy utilization efficiency and reducing operating costs.

[0064] Through the above steps S102-S108, the system parameters of the multi-energy distributed system are obtained, wherein the system parameters include electric load parameters, heat load parameters and cold load parameters. According to the system parameters, an objective function with the lowest system operation cost as the goal is determined, wherein the objective function includes a shiftable load compensation cost item, a transferable load compensation cost item, a reducible load compensation cost, a convertible supply load compensation cost item, and a convertible supply load is a load that can be converted to a load supplied by other energy forms, and the conversion includes at least the conversion between electric energy and thermal energy. The constraints corresponding to the objective function are determined, wherein the constraints include shiftable load constraints, transferable load constraints, reducible load constraints, convertible supply energy constraints, and integrated energy system power balance constraints. The objective function under the constraints is solved using a solver to obtain the dispatching power parameters of the multi-energy distributed system, so as to perform power dispatch according to the dispatching power parameters. In the embodiment of the present invention, not only the cold load, the heat load and the electric load are taken into consideration, but also the shiftable, transferable and reducible loads, and even the convertible supply energy. It can be seen that when determining the power strategy of a multi-energy distributed system, in addition to considering the energy supply situation of the equipment itself, the situations of being able to be dispatched, reduced and converted are also considered, so that the power strategy determined by the multi-energy distributed system is more intelligent and more efficient, thereby solving the technical problem in the related technology that when formulating the power strategy of a multi-energy distributed system, the energy supply situation of the equipment itself is mostly considered, and it is difficult to fully utilize the equipment capacity.

[0065] As an optional embodiment, before determining the constraints corresponding to the objective function, it also includes: determining the translation time corresponding to the shiftable load; determining the transfer time corresponding to the transferable load, the transferable power corresponding to the transferable load at the transfer time, and the reserved power corresponding to the transferable load; determining the reducible time, the duration of the reduction and the number of times the reduction can be made corresponding to the reducible load; constructing the shiftable load compensation cost item and the shiftable load constraint based on the translation time, and constructing the transferable load compensation cost item and the transferable load constraint based on the transfer time, the transferable power and the reserved power, and constructing the reducible load compensation cost item and the reducible load constraint based on the reducible time, the duration of the reduction and the number of times the reduction can be made.

[0066] In this embodiment, it is described how to determine specific adjustment parameters for different types of adjustable loads (i.e., translatable loads, transferable loads, and curtailable loads), and construct corresponding compensation cost items and constraints based on these parameters.

[0067] Among them, the translation time corresponding to the shiftable load is determined, wherein the translation time refers to the specific time point or time period when the shiftable load moves from the original planned time period to the new time period. This is usually determined based on system requirements, load characteristics and user preferences. Among them, the transfer time, transferable power and reserved power corresponding to the transferable load are determined, and the transfer time refers to the specific time point or time period when the transferable load is transferred from one area or part of the time to another area or part of the time. The transferable power is the maximum amount of power that the load can transfer within the transfer time. The reserved power is the amount of power reserved by the device itself during the transfer process in order to maintain system stability or meet the basic needs of users. In addition, the reducible time, the duration of reduction and the number of times that can be reduced corresponding to the reducible load are also determined, wherein the reducible time refers to the specific time point or time period when the load can be reduced. The duration of reduction is the maximum time that the load can be continuously reduced within the reducible time. The number of times that can be reduced is the maximum number of times the load can be reduced in a day or a specific time period. Through the above data, the corresponding compensation cost items and constraints are constructed.

[0068] For the above-mentioned compensation cost items and constraints for shiftable loads, the system may need to pay certain compensation fees to users according to the shifting time to encourage load shifting. At the same time, it is necessary to construct constraints to ensure that the shifting does not violate system rules and is within the reasonable dynamic energy consumption range of the system. For the compensation cost items and constraints for shiftable loads, based on the shifting time, shiftable power and reserved power, the system needs to calculate the compensation cost of the shifted load and construct constraints to ensure the safety and economy of the shifting process. For the compensation cost items and constraints for curtailable loads, the system also needs to determine the compensation cost to compensate for the user's losses for the curtailable loads, and construct constraints to limit the timing, duration and number of curtailments to avoid excessive impact on users. The determination of all adjustment parameters and the construction of constraints should be based on system stability and security. Any adjustment should not endanger the normal operation of the system or the safety of users. By allowing load shifting, transfer and curtailment, the system can respond more flexibly to changes in energy demand and improve energy efficiency.

[0069] As an optional embodiment, determining the reducible time, reduction duration and number of reductions corresponding to the reducible load includes: determining the reducible time corresponding to the reducible load; determining a reduction coefficient corresponding to the reducible time, wherein the reduction coefficient is used to indicate the degree of reduction in user experience after load reduction; and determining the reduction duration and number of reductions in different time periods in the reducible time based on the reduction coefficient.

[0070] In this embodiment, the scheduling strategy for the load that can be reduced is refined, especially how to determine the time that can be reduced, the reduction coefficient, and how to determine the sustainable time and the number of times that can be reduced in different time periods based on these parameters.

[0071] Among them, the curtailable time corresponding to the curtailable load is determined, and the curtailable time refers to the specific time period in which the system or user agrees and allows the load to be curtailed. The selection of these time periods is usually based on factors such as the supply and demand forecast of the system, load characteristics, user preferences, and power grid security and stability. The curtailment coefficient corresponding to the curtailable time is determined. The curtailment coefficient is a quantitative indicator used to indicate the degree of reduction in user experience after the load is curtailed. This coefficient can be based on user satisfaction surveys, historical data analysis, or expert evaluation. The level of the curtailment coefficient directly affects the calculation of compensation costs and user acceptance. The curtailment coefficient can be a fixed value or a function that varies with time to reflect the user's sensitivity to different curtailment levels in different time periods. According to the curtailment coefficient, the curtailment duration and the number of curtailments in different time periods are determined in the curtailable time. That is, after determining the curtailable time and the curtailment coefficient, the system needs to further analyze how to reasonably arrange the specific operations of curtailing the load in these time periods. This includes determining the maximum time (curtailment duration) for which the load can be continuously curtailed in each time period and the number of curtailments that can be made in the time period. In the embodiment of the present application, factors such as the actual needs of users, the stability requirements of the system, and market rules are considered. For example, during peak load periods, it may be necessary to limit the sustainable time and number of reductions to avoid excessive impact on the system; during low load periods, these restrictions can be appropriately relaxed. By introducing reduction coefficients and reasonably determining the sustainable time and number of reductions, the system can adjust the load reduction strategy more flexibly, and considering the user's acceptance, it can reduce the negative impact on user experience, thereby improving user satisfaction. A reasonable load reduction strategy helps optimize the utilization of system resources and reduce system operating costs. By reducing the load in appropriate time periods, the system can meet energy needs more efficiently and reduce energy waste.

[0072] As an optional embodiment, before determining the constraint conditions corresponding to the objective function, it also includes: determining a conversion relationship corresponding to the energy conversion equipment, wherein the energy conversion equipment includes equipment for converting between a first energy and a second energy; when a first energy surplus is generated in a predetermined time period, determining whether there is a demand result for a second energy in the predetermined time period; when the demand result is that there is a demand for the second energy, determining the first energy conversion amount in the time period based on the first energy surplus and the second energy demand; and constructing a convertible supply energy constraint and a convertible supply load compensation cost item based on the first energy conversion amount in the time period.

[0073] In this embodiment, it is explained how to determine the conversion relationship of the energy conversion equipment and the decision-making process of energy conversion under specific conditions (such as the existence of a first energy surplus and a second energy demand in a predetermined period of time), and accordingly construct the corresponding supply energy constraint and load compensation cost item. Since the energy conversion equipment refers to equipment that can convert one form of energy (first energy) into another form (second energy), such as a cogeneration unit, a gas turbine, a water electrolysis hydrogen production device, etc., it is possible to convert energy.

[0074] In this process, the conversion relationship corresponding to the energy conversion device is determined. The conversion relationship is a mathematical model that describes this conversion process, which usually includes the relationship between parameters such as input energy, output energy, and conversion efficiency. In the case where the first energy surplus is generated in a predetermined period of time, that is, the first energy generated by the system in an arbitrarily set time period exceeds the current or expected demand, the remaining energy is the first energy surplus. The surplus can be converted. At this time, it can be determined whether there is a demand result for the second energy demand in the predetermined period of time. The system needs to analyze whether there is a demand for the second energy in the predetermined period of time. If there is a second energy demand, the system will continue to make the next energy conversion decision. If there is no demand, it may choose to store or use the first energy surplus for other purposes, or directly avoid the generation of the excess energy.

[0075] When there is a second energy demand, the first energy conversion amount in the time period can be determined based on the first energy surplus and the second energy demand. The conversion amount should meet the demand of the second energy, while making the best use of the first energy surplus to avoid waste. Therefore, the convertible supply energy constraint and the convertible supply load compensation cost item can be constructed based on the first energy conversion amount in the time period, so that it complies with the constraints such as energy balance and conversion efficiency and can consider its conversion cost.

[0076] Optionally, the above process can be dynamically adjusted, that is, the energy conversion decision should be able to be dynamically adjusted according to real-time or predicted data. For example, when the first energy surplus or the second energy demand changes, the system can re-evaluate and adjust the conversion strategy.

[0077] In this way, energy conversion can be carried out reasonably, and the surplus of the first energy can be fully utilized to meet the demand of the second energy, thereby improving the energy utilization efficiency of the entire system. It can reduce operating costs, build a convertible supply load compensation cost item, and encourage all parties to actively participate in the energy conversion process and reduce the operating costs of the system. It can also enhance system flexibility. The energy conversion equipment provides more flexibility for the system, allowing the system to better cope with changes and uncertainties in energy demand.

[0078] As an optional embodiment, before determining the constraint conditions corresponding to the objective function, it also includes: determining the load equipment in the multi-energy distributed system, wherein the load equipment includes: rigid heat load, shiftable heat load, reducible heat load, rigid cooling load, shiftable cooling load, and reducible cooling load; the heat load parameters include a first heat load parameter corresponding to the rigid heat load, a second heat load parameter corresponding to the shiftable heat load, and a third heat load parameter corresponding to the reducible heat load; the cooling load parameters include a first cooling load parameter corresponding to the rigid cooling load, a second cooling load parameter corresponding to the shiftable cooling load, and a third cooling load parameter corresponding to the reducible cooling load; based on the first heat load parameter, the second heat load parameter, and the third heat load parameter, a heat power balance constraint is determined; based on the first cooling load parameter, the second cooling load parameter, and the third cooling load parameter, a cooling power balance constraint is determined; and based on the electric load parameter, an electric power balance constraint is determined; based on the heat power balance constraint, the cooling power balance constraint, and the electric power balance constraint, a power balance constraint of the integrated energy system is determined.

[0079] In this embodiment, multiple loads are involved. Rigid heat load refers to heat demand that must be met and cannot be changed in usage time and load amount, such as residential hot water supply. Shiftable heat load refers to heat demand that can be flexibly adjusted within a certain time range, such as some industrial heating processes. Reducible heat load refers to heat demand that can be appropriately reduced or cancelled under certain conditions, such as some non-essential heating services. Rigid cooling load, shiftable cooling load, and reducible cooling load: similar to heating load, but for cooling demand, such as air conditioning system.

[0080] Different types of load equipment correspond to different load parameters, which are the basis for constructing power balance constraints. Thermal power balance constraints ensure that the total demand of all thermal loads (rigid, movable, and reducible) can be met by the supply of heat sources, while taking into account thermal energy conversion efficiency and transmission losses. Cold power balance constraints, similarly, ensure that the demand for cold loads is met, which may involve efficiency optimization of the refrigeration system. Electric power balance constraints: Taking into account the electrical load (including power demand for heat conversion, cold conversion, and other electrical equipment), ensuring the balance of power supply and demand may require consideration of factors such as grid access, energy storage devices, and distributed generation. The power balance constraint of the integrated energy system integrates the power balance constraints of the three energy systems of heat, cold, and electricity to form a global power balance constraint for the integrated energy system. This helps to achieve optimal allocation and efficient utilization of resources in complex and changing energy demands.

[0081] By distinguishing different types of loads and allowing them to be shifted or reduced within a certain range, the system can respond more flexibly to external changes. Through detailed power balance analysis and constraint setting, it can better prevent energy shortages or surpluses and ensure the stable operation of the system.

[0082] As an optional embodiment, an objective function with the goal of minimizing the system operating cost is determined based on system parameters, including: determining pre-power scheduling parameters; determining, based on the pre-power scheduling parameters, whether there is a transfer result of a detachable device in the multi-energy distributed system; when the transfer result is that there is a detachable device, determining whether there is a demand result for energy demand corresponding to the detachable device in the short-distance power grid system, wherein the short-distance power grid system is a system within a predetermined range from the multi-energy distributed system; when the demand result is that there is an energy demand corresponding to the detachable device, the detachable device is transferred to the short-distance power grid system for use by the short-distance power grid system.

[0083] In this embodiment, the process of implementing a strategy for optimizing operating costs based on system parameters is described. The strategy matches the energy demand of a short-distance power grid system through pre-analysis of power dispatch and identification of remotely-dispatched equipment.

[0084] Among them, the pre-power scheduling parameters can be the previously estimated power scheduling parameters. Through the pre-power scheduling parameters, it is possible to determine whether there are any removable devices in the multi-energy distributed system, that is, whether the system can still meet the corresponding power and setting requirements after a certain device is removed. For example, the system can identify devices that may be idle or underloaded under the current scheduling plan. They can be temporarily removed from the original system under certain conditions to support other systems in need and optimize resource utilization.

[0085] For the identified removable devices, the system further analyzes the energy demand of the short-distance power grid system (such as nearby communities, industrial areas, etc.) within the predetermined range of the multi-energy distributed system. By comparing the type and capacity of the removable device with the energy demand of the short-distance power grid system, it is determined whether there is a matching energy demand. If there is a matching energy demand, the system performs a relocation operation and temporarily transfers the removable device to the short-distance power grid system to meet its energy demand. This relocation can be a physical move (such as a mobile generator) or an energy transmission achieved through grid interconnection. By relocating idle or low-load equipment to the short-distance power grid system with demand, the utilization rate of the equipment is improved and the idle cost of the equipment in the original system is reduced. At the same time, it is also possible to obtain additional economic benefits through energy trading, further reducing the system operation cost.

[0086] This strategy promotes the flexible configuration and sharing of energy between different systems, and improves the overall efficiency and reliability of the entire regional energy system. By optimizing resource allocation, energy waste is reduced and energy utilization efficiency is improved. It also allows the transfer of equipment between different systems, enhancing the flexibility and adaptability of multi-energy distributed systems. When faced with fluctuations in energy demand or emergencies, the system can respond more quickly and adjust energy supply strategies.

[0087] As an optional embodiment, after using a solver to solve the objective function under constraints and obtaining the dispatching power parameters of the multi-energy distributed system, it also includes: controlling the multi-energy distributed system to perform power dispatch according to the dispatching power parameters; determining the system feedback index after power dispatch operation with the dispatching power parameters; and adjusting the dispatching power parameters according to the system feedback index to obtain updated power parameters.

[0088] In this embodiment, it is explained that the multi-energy distributed system is controlled to perform power dispatch according to the dispatch power parameters, that is, after the optimal dispatch power parameters under the objective function that meets the constraint conditions are solved by the solver, the system needs to control the working state of each energy production, conversion and storage device according to these parameters to achieve the predetermined power output or input. Afterwards, the system feedback index after the power dispatch operation is performed with the dispatch power parameters is determined, that is, in the actual power dispatch process, the system will continuously collect various operating data, such as actual power generation, energy conversion efficiency, equipment operating status, etc., and calculate the system feedback index based on these data. These indexes can reflect the efficiency, stability, economy and other aspects of the system operation, and are an important basis for evaluating the dispatch effect. According to the system feedback index, the dispatch power parameters are adjusted to obtain updated power parameters. According to the system feedback index, the system can identify the shortcomings in the current dispatch scheme, such as overload of certain equipment, energy waste, and excessive cost. In response to these problems, the system will make corresponding adjustments to the dispatch power parameters to optimize the dispatch scheme. The adjusted power parameters will serve as a new dispatch basis to guide the future power dispatch work of the system. By collecting system feedback indexes in real time and adjusting the dispatch power parameters accordingly, the system can promptly detect and resolve problems in operation, ensuring that each device operates in the optimal state, thereby improving the overall efficiency and stability of the system. In the face of uncertain factors such as energy demand fluctuations and emergencies, the system can quickly respond to changes by adjusting the dispatch power parameters to ensure the stable operation of the system.

[0089] Based on the above embodiments and optional embodiments, an optional implementation is provided, which is described in detail below.

[0090] In an optional implementation manner of the present invention, a power scheduling method for a multi-energy distributed system is provided, which includes an established regional energy Internet multi-energy complementary day-ahead optimization operation scheduling model, which can perform day-ahead optimization scheduling of the energy Internet based on predicted output and load information of new energy such as photovoltaics, so as to optimize the operation economy of the energy Internet.

[0091] Figure 2 This is a flow chart for solving a multi-energy complementary day-ahead optimization operation scheduling model for a regional integrated energy system provided by an optional implementation mode of the present invention. Figure 3 A schematic diagram of wind turbine and photovoltaic prediction data of a regional integrated energy system multi-energy complementary day-ahead optimization operation scheduling model provided by an optional implementation mode of the present invention, Figure 4 A schematic diagram of the electric load forecasting data of the multi-energy complementary day-ahead optimization operation scheduling model of the regional integrated energy system provided by an optional implementation mode of the present invention, Figure 5 This is a schematic diagram of heat load forecasting data of a multi-energy complementary day-ahead optimization operation scheduling model for a regional integrated energy system provided in an optional embodiment of the present invention. Figure 6 This is a schematic diagram of cold load forecast data of a multi-energy complementary day-ahead optimization operation scheduling model for a regional integrated energy system provided in an optional embodiment of the present invention. Figure 7 A schematic diagram of the time-of-use electricity price of a regional integrated energy system multi-energy complementary day-ahead optimization operation scheduling model provided by an optional implementation mode of the present invention, Figure 8 A schematic diagram of the output of each device of the power load supplied by the multi-energy complementary day-ahead optimization operation scheduling model of the regional integrated energy system provided by an optional implementation mode of the present invention, Fig. 9 A schematic diagram of the output of each device of the supply thermal load of the multi-energy complementary day-ahead optimization operation scheduling model of the regional comprehensive energy system provided by an optional implementation mode of the present invention, Fig.10 A schematic diagram of the output of each device of the cooling load supplied by the multi-energy complementary day-ahead optimization operation scheduling model of the regional integrated energy system provided by an optional implementation mode of the present invention, Fig.11 A schematic diagram of the shiftable electric load of the multi-energy complementary day-ahead optimization operation scheduling model of the regional integrated energy system provided by an optional implementation mode of the present invention, Fig.12 A schematic diagram of transferable electric loads of a regional integrated energy system multi-energy complementary day-ahead optimization operation scheduling model provided by an optional implementation mode of the present invention, Fig.13 A schematic diagram of the electric load that can be reduced in a regional integrated energy system multi-energy complementary day-ahead optimization operation scheduling model provided in an optional implementation mode of the present invention, Fig.14 A schematic diagram of the total electric load change of the multi-energy complementary regional integrated energy system day-ahead optimization operation scheduling model provided by an optional implementation mode of the present invention is described below in conjunction with the figure. The optimization operation scheduling model includes the following steps:

[0092] A. Constraints. The regional energy Internet multi-energy complementary optimization operation model established by the present invention mainly includes the following constraints: power transmission constraints between the power grid and the integrated energy system; photovoltaic power output capacity constraints; gas turbine operation constraints; fuel cell operation constraints; gas boiler operation constraints; electric boiler operation constraints; electric refrigerator operation constraints; absorption refrigerator operation constraints; energy storage operation constraints; shiftable load constraints; transferable load constraints; reducible load constraints; integrated energy system power balance constraints.

[0093] B. Objective function. The multi-energy complementary optimization operation model of the regional energy Internet built by the present invention takes the system cost as the target. The daily cost of the energy Internet includes: the electricity purchase cost of the comprehensive energy system; the operating cost of distributed power supply; the operating cost of gas equipment; the operating cost of electric boilers, electric refrigerators and absorption refrigerators; the operating cost of energy storage equipment; the compensation cost of loads that can be translated; the compensation cost of loads that can be transferred; the compensation cost of loads that can be reduced. The objective function of the multi-energy complementary optimization operation model of the regional energy Internet built by the present invention is:

[0094]

[0095] Among them, F gird is the electricity purchase cost of the energy internet; F DG is the operating cost of distributed power sources such as photovoltaics and wind turbines; F GT is the operating cost of the gas turbine; F GB is the operating cost of the gas boiler; F FC is the operating cost of the fuel cell; F EB is the operating cost of the electric boiler; F EC is the operating cost of the electric refrigerator; F AC is the operating cost of the absorption chiller; F BAT is the operating cost of the battery; F TS is the operating cost of the heat storage tank; F CS is the operating cost of the cold storage tank; Compensation for portable electrical loads; Compensation for shiftable heat loads; Compensation for movable cooling load; compensation costs for shiftable loads; Compensation for the reduction of electrical load; To compensate for the reduced heat load; This can reduce cooling load compensation costs.

[0096] C. Model solution. The regional energy internet multi-energy complementary optimization operation model described in the present invention contains multiple energy forms and energy conversions, has many decision variables, and has a large solution space. In this regard, the present invention uses the Yalmip toolbox and Cplex solver based on Matlab software to solve.

[0097] The specific implementation is as follows:

[0098] Step 1: The constraints of the day-ahead optimization operation scheduling model described in the present invention are mainly composed of the following steps:

[0099] Step 11: Power grid purchase and sales capacity constraints. The power purchased or sold by the Energy Internet to the power system cannot exceed the maximum allowable power value of the line, that is:

[0100]

[0101] Where P link.max is the maximum operating power value of the line; It is the state of purchasing and selling electricity.

[0102] Step 12: Output capacity constraints of distributed power sources such as photovoltaics and wind turbines. The maximum active power output of the photovoltaic power source cannot exceed the photovoltaic power source capacity, that is:

[0103] 0≤P pv (t)≤S pv (3)

[0104] Step 13: Gas turbine operation constraints. Gas turbines mainly generate electricity and heat energy through natural gas. The relationship between generated electricity and heat power is:

[0105]

[0106] In the formula, Q GT(t) P is the heat energy generated by the gas turbine; GT(t) is the electrical power output of the gas turbine; η GT is the gas turbine efficiency; η l is the gas turbine heat loss coefficient.

[0107] Gas turbines consume natural gas while generating electricity and heat. The relationship between the amount of natural gas consumed and the output power is:

[0108]

[0109] Where V GT(t) is the natural gas consumption of gas turbine; L NG is the lower calorific value of natural gas, and △t is the scheduling interval.

[0110] Considering that the gas turbine output cannot exceed its upper and lower output limits, that is:

[0111] P GT.min ≤P GT (t)≤P GT.max (6)

[0112] Among them, P GT.min is the minimum output of the gas turbine; P GT.max The maximum output of the gas turbine.

[0113] The gas turbine output should also meet the up and down climbing speeds, that is:

[0114] -r GT.down Δt≤P GT (t+1)-P GT (t)≤r GT.up Δt (7)

[0115] In the formula, r GT.up , r GT.down It is the maximum up and down climbing speed of the gas turbine.

[0116] Step 14: Fuel cell operation constraints. Fuel cell output constraints should satisfy:

[0117] P FC.min ≤P FC (t)≤P FC.max (8)

[0118] Where P FC(t) is the output of the fuel cell at time t; P FC.max , P FC.min are the maximum and minimum output power of the fuel cell respectively. The constraints of the maximum up and down ramp rates are:

[0119]

[0120] In the formula, r FC.up , r FC.down is the maximum value of the fuel cell up / down ramp rate, It is the fuel cell operation state variable, a 0-1 variable, 0 means the fuel cell is not working, and 1 means it is working.

[0121] Step 15: Gas boiler operation constraints. The output of the gas boiler cannot exceed the upper and lower limits of the output, so it should meet the following requirements:

[0122]

[0123] The maximum up and down climbing speed constraints are:

[0124]

[0125] Among them, r GB.up 、r GB.down is the maximum value of the up and down climbing rate of the gas boiler, It is the gas boiler operation status variable, a 0-1 variable, 0 means the gas boiler is not working, 1 means it is working.

[0126] Step 16: Electric boiler operation constraints. The electric boiler input should be less than its maximum input power, that is:

[0127]

[0128] in, is the maximum input power of the electric boiler. The maximum up and down ramp rate constraints are:

[0129]

[0130] Among them, r EB.up 、r EB.down The upward and downward climbing rates of electric boilers are the largest.

[0131] Step 17: Electric refrigerator operation constraints. The cooling power output of the electric refrigerator cannot exceed the maximum output power of the electric refrigerator:

[0132]

[0133] in, is the maximum output power of the electric refrigerator. The maximum up and down ramp rate constraints are:

[0134]

[0135] Among them, r EC.up 、r EC.down It is the maximum value of the upward and downward climbing rate of the electric refrigerator.

[0136] Step 18: Absorption chiller operation constraints. The absorption chiller supplies the cooling load of the energy internet, and its output cooling power should be within the limit, that is:

[0137]

[0138] in, is the maximum output power of the absorption chiller. The output power of the absorption chiller is also affected by the climbing speed, that is:

[0139]

[0140] Among them, r AC.up is the maximum upward ramp rate of the absorption chiller; r AC.downis the maximum downward ramp rate of the absorption chiller.

[0141] Step 19: Energy storage equipment operation constraints. Taking batteries as an example, overcharging and overdischarging of batteries will affect the operating state of the batteries. Therefore, the battery state of charge needs to be limited within the safety and reliability range, that is:

[0142]

[0143] Among them, S BAT.max , S BAT.min The upper and lower safety limits of the battery's state of charge.

[0144] The battery has only one working state in a period of time, namely:

[0145]

[0146] They represent the charging and discharging status of the energy storage device, respectively, and are both 0-1 variables; =1 indicates that charging is not performed; =1 indicates a discharge operation; in the same period of time, the device can only charge or discharge or neither charge nor discharge, so the product of the two is 0.

[0147] When batteries participate in the energy internet optimization operation, the state of charge needs to be equal at the beginning and end of the dispatch cycle, that is, it should meet the following requirements:

[0148]

[0149] in, The state of charge of the battery at the beginning of the scheduling cycle when the battery participates in the energy Internet optimization operation. In order to ensure the working life of the battery and improve the overall economic efficiency and reliability of the energy Internet operation, the battery cannot be charged and discharged too much, that is, it should meet the following requirements:

[0150]

[0151] Among them, E BAT is the rated capacity of the battery. The number of times the battery is charged and discharged will affect the operating life of the battery, and thus affect the overall equipment investment cost of the energy Internet. Therefore, it is necessary to control the number of times the energy storage device is charged and discharged to ensure the working time of the energy storage device.

[0152]

[0153] in, The number of times the energy storage device can be safely charged; The number of safe discharges of energy storage equipment.

[0154] Step 110: Constraints on movable loads. A movable load is a type of flexible load, which can be completely shifted to relieve the load pressure of the energy Internet for a period of time. shift For example, L shift It can be represented by a set of vectors, which have a certain continuity, where t s is the initial time of the translatable load, t D is the continuous time of the translatable load.

[0155]

[0156] Assume that the time interval that the movable load can be moved to is [t sh- ,t sh- +1,t sh+ ], considering that the translatable load needs to be translated as a whole, the initial value of the translatable time interval must be met. If not, some translatable loads will exceed the translatable time interval.

[0157] S shift ={[t sh- ,t sh- +1,…,t sh+ -t D +1],t s} (twenty four)

[0158] The initial value of the shiftable time interval is whether to perform load shifting. Available vector express, It means that load translation is performed under this initial value. Indicates that no load translation is performed at this initial value.

[0159]

[0160] In order to facilitate energy Internet scheduling, the shiftable load is generally shifted only once. Constraints should be satisfied

[0161]

[0162] Step 111: Transferable load constraint. Transferable load means that the load in a certain period can be transferred to another period, thereby achieving the purpose of peak load reduction and valley filling. tran For example, X tran Indicates whether load transfer is performed in the corresponding period. Indicates that load transfer was performed during this period. Indicates that no load transfer was performed during this period.

[0163]

[0164] At the same time, there are certain constraints on the transfer power. The transfer load cannot be completely transferred to avoid affecting the daily life of park users. Therefore, the constraints should be met:

[0165]

[0166] in, is the maximum transferable value; is the minimum value that can be transferred. If the duration of the transferable load is not limited, the power equipment in the park will be frequently switched on and off, affecting the operating life of the user's power equipment. To this end, it is necessary to limit the minimum duration of the transferable load, that is:

[0167]

[0168] Since the transferable load operation is only to transfer the load without load reduction, the total amount of load before and after the load transfer will not change, that is:

[0169]

[0170] in, is the total power before the transferable load is transferred.

[0171] Step 112: Constraints on load that can be reduced. Load that can be reduced refers to the direct cutting out of loads to ensure the balance between the source and the load of the energy Internet. cut For example:

[0172]

[0173] Among them, X cut Indicates whether the load is reduced during the corresponding period. Indicates that load reduction was performed during this period. Indicates that no load reduction operation was performed during this period.

[0174]

[0175] Reducing load will affect user comfort. For example, reducing the cooling load will cause the indoor temperature to be too high in summer, while reducing the heating load will cause the indoor temperature to be too low in winter, which will lead to poor user experience. To this end, you need to set the minimum duration and maximum duration of load reduction and the upper limit of the number of times the load can be reduced:

[0176]

[0177]

[0178]

[0179] Step 113: Energy Internet power balance constraints. The power balance constraints mainly include electric power balance, thermal power balance and cold power balance. The electric power balance of the Energy Internet is:

[0180]

[0181] The thermal power balance of the energy internet is:

[0182]

[0183] in, is the rigid heat load; is a translatable heat load; The heat load can be reduced. The cooling power balance of the energy Internet is:

[0184]

[0185] in, The power required for rigid cooling load; It is a movable cooling load; To reduce cooling load.

[0186] Step 2: The regional energy internet multi-energy complementary optimization operation model of the present invention takes the system cost as the target, and the objective function of the energy internet daily cost includes the following steps:

[0187] Step 21: Energy Internet electricity purchase cost. When the Energy Internet lacks electricity, it needs to purchase electricity from the power grid. When the Energy Internet has sufficient electricity, it can sell electricity to the power grid. Therefore, the energy Internet electricity purchase cost in a dispatch cycle is related to the amount of electricity purchased and sold.

[0188]

[0189] in, The price of electricity purchased from the grid for the energy internet; is the price at which the Energy Internet sells electricity to the power grid. Therefore, the cost of purchasing electricity for the Energy Internet is:

[0190] F grid =F pur -F sell (40)

[0191] Step 22: Operation cost of distributed power sources such as photovoltaics and wind turbines. Distributed power sources in the energy internet are generally composed of new energy sources such as photovoltaics and wind turbines. In the energy internet, only photovoltaic and wind turbine power sources are considered. The total operation cost of photovoltaic and wind turbine distributed power sources in one scheduling cycle is:

[0192]

[0193] Among them, K wind K is the electricity cost of wind distributed power generation; pv is the electricity cost of photovoltaic distributed power generation.

[0194] Step 23: Gas equipment operating cost. The operating cost of gas equipment (gas turbine, gas boiler and fuel cell) in one scheduling cycle is:

[0195]

[0196] in, is the natural gas consumption of the gas turbine; K GT is the electricity cost of the gas turbine; is the natural gas consumption of gas boiler; K GB The electricity cost of the gas boiler; is the natural gas consumption of the fuel cell; K FC K is the electricity cost of the fuel cell; gas For natural gas costs.

[0197] Step 24: Operating costs of electric boilers, electric refrigerators and absorption refrigerators. The operating costs of electric boilers, electric refrigerators and absorption refrigerators in one scheduling cycle are:

[0198]

[0199] Among them, K EB is the operating cost coefficient of the electric boiler; K EC is the operating cost coefficient of the electric refrigerator; K AC is the operating cost coefficient of the absorption chiller.

[0200] Step 25: Operating cost of energy storage equipment. In one dispatch cycle, the operating cost of energy storage equipment (batteries, heat storage tanks, cold storage tanks) is:

[0201]

[0202] Among them, K cha K is the cost coefficient of energy storage equipment; dis is the discharge cost coefficient of energy storage equipment.

[0203] Step 26: Compensation costs for movable loads. The compensation costs for movable loads (movable electrical load, movable heat load, and movable cooling load) are:

[0204]

[0205] in, is the cost factor of the movable load; is the overall transferable electrical load; is the transferable heat load cost coefficient, is the overall translatable heat load; is the transferable cooling load cost coefficient, is the overall transferable cooling load.

[0206] Step 27: Transferable load compensation fee. The transferable load compensation fee is:

[0207]

[0208] in, is the transferable load cost coefficient.

[0209] Step 28: Compensation for reducible loads. The compensation for reducible loads (reducible electrical load, reducible heating load and reducible cooling load) is:

[0210]

[0211] in, To reduce the electricity load cost factor; To reduce the heat load cost factor;

[0212] is the cooling load cost reduction factor. i is the reduction factor of the electric load that can be reduced; θ *i is the heat load reduction factor that can be reduced; is the cooling load reduction factor that can be reduced; They are the reduction states of electrical load, heat load and cooling load, 0 means no reduction and 1 means reduction; Respectively represent the power reduction values ​​of electrical load, thermal load and cooling load that can be reduced; They represent the reduction in electrical load, thermal load and cooling load reduction compensation costs respectively.

[0213] Step 3: Set the basic conditions for solving the regional energy Internet multi-energy complementary optimization operation model of the present invention. Among them, the wind turbine and photovoltaic prediction data are as follows: Figure 3 As shown; the forecast data of electric load, heat load and cooling load are as follows Figures 4 to 6 As shown; the natural gas price is 3.23￥ / m3, the combustion heat value is 9.78KM / m3, and the time-of-use electricity price is as follows Figure 7 shown.

[0214] Step 4: The regional energy internet multi-energy complementary optimization operation model described in the present invention contains multiple energy forms and energy conversions, with many decision variables and a large solution space. In this regard, the present invention uses the Yalmip toolbox and Cplex solver based on Matlab software to solve the problem. Figure 8 As shown in the figure, it can be seen that through the coordinated optimization of multiple energy complementarities in the energy Internet, the consumption of new energy has been realized and the construction of a new power system has been promoted. The output thermal power of gas boilers, gas turbines, electric boilers, and heat storage tanks is shown in the figure. Fig. 9 As shown; the output of absorption refrigerator, electric refrigerator and cold storage tank is as follows Fig.10 Taking the electric load as an example, the changes of the electric load that can be translated, the electric load that can be transferred, the electric load that can be reduced, and the total electric load are shown in Figures 11 to 14 As shown in Figure 2, it can be seen that the purpose of peak shaving and valley filling is achieved by using flexible loads to participate in the demand-side response of the energy Internet.

[0215] Through the above optional implementation, at least the following beneficial effects can be achieved:

[0216] (1) Compared with the traditional distributed energy system, the multi-energy complementary distributed energy system provided by the present invention is the application of the inheritance concept in engineering, which gradually expands the application scope of the multi-energy complementary system. Specifically, the system includes a variety of different energy resource inputs and has many different output and transportation forms. It is a "regional energy interconnection" system. It is not just a simple superposition of multiple resources, but at the system level, it targets the comprehensive complementary utilization of different energy sources and coordinates the mutual conversion between various energy sources, so as to achieve the most reasonable effect and benefit;

[0217] (2) The regional integrated energy system multi-energy complementary day-ahead optimal operation and scheduling model provided by the present invention contains multiple energy forms and energy conversions. It optimizes the day-ahead integrated energy system based on the predicted output and load information of new energy sources such as photovoltaics, so that the integrated energy system operates at the most economical efficiency, with many policy variables and a large solution space.

[0218] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0219] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods of various embodiments of the present invention.

[0220] Example 2

[0221] According to an embodiment of the present invention, a device for implementing the power scheduling method of the multi-energy distributed system is also provided. Fig.15 is a structural block diagram of a power dispatching device for a multi-energy distributed system according to an embodiment of the present invention. Fig.15 As shown, the device includes: an acquisition module 1502, a first determination module 1504, a second determination module 1506 and a third determination module 1508. The device is described in detail below.

[0222] The acquisition module 1502 is used to obtain the system parameters of the multi-energy distributed system, wherein the system parameters include electric load parameters, heat load parameters and cooling load parameters; the first determination module 1504 is connected to the above-mentioned acquisition module 1502, and is used to determine the objective function with the lowest system operation cost as the goal based on the system parameters, wherein the objective function includes a shiftable load compensation cost item, a transferable load compensation cost item, a reducible load compensation cost, a convertible supply load compensation cost item, and the convertible supply load is a load that can be converted into a load supplied by other energy forms, and the conversion at least includes the conversion between electric energy and thermal energy; the second determination module 1506 is connected to the above-mentioned first determination module 1504, and is used to determine the constraint conditions corresponding to the objective function, wherein the constraints include shiftable load constraints, transferable load constraints, reducible load constraints, convertible supply energy constraints, and comprehensive energy system power balance constraints; the third determination module 1508 is connected to the above-mentioned second determination module 1506, and is used to use a solver to solve the objective function under the constraint conditions to obtain the dispatching power parameters of the multi-energy distributed system, so as to perform power dispatch according to the dispatching power parameters.

[0223] It should be noted here that the above-mentioned acquisition module 1502, the first determination module 1504, the second determination module 1506 and the third determination module 1508 correspond to steps S102 to S108 in the power scheduling method for implementing a multi-energy distributed system, and the instances and application scenarios implemented by the multiple modules are the same as the corresponding steps, but are not limited to the contents disclosed in the above-mentioned embodiment 1.

[0224] Example 3

[0225] According to another aspect of an embodiment of the present invention, there is also provided an electronic device, comprising: a processor; and a memory for storing processor executable instructions, wherein the processor is configured to execute instructions to implement any of the above-mentioned power scheduling methods for a multi-energy distributed system.

[0226] Example 4

[0227] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is also provided. When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute any of the above-mentioned power scheduling methods for a multi-energy distributed system.

[0228] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0229] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0230] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0231] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0232] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0233] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0234] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A power dispatching method for a multi-energy distributed system, characterized in that: include: Acquire system parameters of the multi-energy distributed system, wherein the system parameters include electric load parameters, heat load parameters, and cooling load parameters; According to the system parameters, an objective function with the goal of minimizing the system operation cost is determined, wherein the objective function includes a shiftable load compensation cost item, a transferable load compensation cost item, a reducible load compensation cost item, and a convertible supply load compensation cost item, wherein the convertible supply load is a load that can be converted to be supplied by other energy forms, and the conversion at least includes the conversion between electrical energy and thermal energy; Determine the constraint conditions corresponding to the objective function, wherein the constraint includes a shiftable load constraint, a transferable load constraint, a curtailable load constraint, a convertible supply energy constraint, and a comprehensive energy system power balance constraint; A solver is used to solve the objective function under the constraint conditions to obtain the dispatching power parameters of the multi-energy distributed system, so as to perform power dispatch according to the dispatching power parameters.

2. The method according to claim 1, characterized in that Before determining the constraint conditions corresponding to the objective function, the method further includes: determining a translation time corresponding to the translatable load; Determining a transfer time corresponding to the transferable load, a transferable power corresponding to the transferable load at the transfer time, and a reserved power corresponding to the transferable load; Determining a curtailable time, a curtailable duration, and a curtailable number of times corresponding to the curtailable load; Based on the shifting time, the shiftable load compensation cost item and the shiftable load constraint are constructed, and based on the transfer time, the transferable power, and the reserved power, the shiftable load compensation cost item and the shiftable load constraint are constructed, and based on the reducible time, the reduction duration, and the reducible number of times, the reducible load compensation cost item and the reducible load constraint are constructed.

3. The method according to claim 2, characterized in that Determining the curtailable time, curtailment duration, and curtailment times corresponding to the curtailable load, including: determining a curtailable time corresponding to the curtailable load; Determine a reduction coefficient corresponding to the load reduction time, wherein the reduction coefficient is used to indicate the reduction in the user's corresponding experience level after the load reduction; According to the reduction coefficient, the reduction duration and the number of reductions in different time periods in the reducible time are determined.

4. The method according to claim 1, characterized in that: Before determining the constraint conditions corresponding to the objective function, the method further includes: Determining a conversion relationship corresponding to an energy conversion device, wherein the energy conversion device includes a device for converting between a first energy source and a second energy source; In the case where a first energy surplus is generated in a predetermined time period, determining whether there is a demand result of a second energy demand in the predetermined time period; When the demand result indicates that there is a demand for the second energy, determining the first energy conversion amount in the time period according to the first energy surplus and the second energy demand; The convertible supply energy constraint and the convertible supply load compensation cost item are constructed according to the first energy conversion amount in the time period.

5. The method according to claim 1, characterized in that Before determining the constraint conditions corresponding to the objective function, the method further includes: Determine the load equipment in the multi-energy distributed system, wherein the load equipment includes: rigid heat load, shiftable heat load, reducible heat load, rigid cooling load, shiftable cooling load, reducible cooling load; The heat load parameter includes a first heat load parameter corresponding to the rigid heat load, a second heat load parameter corresponding to the shiftable heat load, and a third heat load parameter corresponding to the reducible heat load; the cold load parameter includes a first cold load parameter corresponding to the rigid cold load, a second cold load parameter corresponding to the shiftable cold load, and a third cold load parameter corresponding to the reducible cold load; a heat power balance constraint is determined based on the first heat load parameter, the second heat load parameter, and the third heat load parameter; a cold power balance constraint is determined based on the first cold load parameter, the second cold load parameter, and the third cold load parameter; and an electric power balance constraint is determined based on the electric load parameter; The power balance constraint of the integrated energy system is determined based on the thermal power balance constraint, the cold power balance constraint, and the electric power balance constraint.

6. The method according to claim 1, characterized in that Based on the system parameters, an objective function with the goal of minimizing the system operation cost is determined, including: Determining advance power scheduling parameters; Determining, based on the pre-power scheduling parameters, whether there is a removal result of a device that can be removed in the multi-energy distributed system; In the case where the adjustment result is that there is an adjustable device, determining whether there is a demand result of energy demand corresponding to the adjustable device in the short-distance power grid system, wherein the short-distance power grid system is a system within a predetermined range from the multi-energy distributed system; When the demand result is that there is an energy demand corresponding to the remote device, the remote device is transferred to the short-distance power grid system for use by the short-distance power grid system.

7. The method according to any one of claims 1 to 6, characterized in that: After using a solver to solve the objective function under the constraint conditions to obtain the dispatching power parameters of the multi-energy distributed system, the method further includes: Controlling the multi-energy distributed system to perform power dispatch according to the dispatch power parameter; Determining a system feedback index after power dispatch operation is performed using the dispatch power parameters; The scheduling power parameter is adjusted according to the system feedback index to obtain an updated power parameter.

8. A power dispatching device for a multi-energy distributed system, characterized in that: include: An acquisition module, used to acquire system parameters of a multi-energy distributed system, wherein the system parameters include electric load parameters, heat load parameters and cooling load parameters; A first determination module is used to determine an objective function with the goal of minimizing the system operation cost according to the system parameters, wherein the objective function includes a shiftable load compensation cost item, a transferable load compensation cost item, a reducible load compensation cost item, and a convertible supply load compensation cost item, wherein the convertible supply load is a load that can be converted to be supplied by other energy forms, and the conversion at least includes the conversion between electric energy and thermal energy; A second determination module is used to determine the constraint conditions corresponding to the objective function, wherein the constraints include a shiftable load constraint, a transferable load constraint, a curtailable load constraint, a convertible supply energy constraint, and a comprehensive energy system power balance constraint; The third determination module is used to use a solver to solve the objective function under the constraint conditions to obtain the dispatching power parameters of the multi-energy distributed system so as to perform power dispatch according to the dispatching power parameters.

9. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the power scheduling method for a multi-energy distributed system as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the power scheduling method for a multi-energy distributed system as described in any one of claims 1 to 7.

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