Optimized scheduling method and device of power system, electronic equipment and storage medium

Abstract

The embodiment of the invention discloses an optimal scheduling method and device for a power system, electronic equipment and a storage medium. The method comprises the following steps: acquiring historical operation data and power consumption data of the power system and current environment data at the current moment; inputting the historical operation data, the power consumption data and the environment data into a pre-established optimization scheduling model, and obtaining a net present value of the power system according to an upper layer model of the optimization scheduling model; and inputting the net present value into a lower-layer model of the optimal scheduling model, obtaining an optimal scheduling strategy based on the lower-layer model and the net present value, and controlling the operation of the power system according to the optimal scheduling strategy. According to the method, the maximum energy efficiency is obtained while it is ensured that the photovoltaic power generation capacity meets the load requirement, the power system can more effectively configure resources, and the energy utilization efficiency is improved.

Description

Technical Field

[0001] Embodiments of the present invention relate to the technical field of new energy power systems, and in particular to an optimization scheduling method, device, electronic equipment and storage medium for a power system. Background Art

[0002] With the transformation of the global energy structure, distributed photovoltaic power generation, as a clean and renewable energy form, has received widespread attention. Especially on the user side, the power system of distributed photovoltaic power generation can not only provide electricity for the user itself, but also provide additional electricity to power external equipment to obtain some additional power benefits. Power system optimization and dispatch refers to the reasonable arrangement of the operation mode and load distribution of the generator set according to the load demand and power generation capacity to ensure the stable operation and efficient power supply of the power system. It involves multiple aspects of consideration, including the coordinated work of various links such as power generation, transmission, and distribution.

[0003] On the user side, the optimal dispatch of distributed photovoltaic power generation systems involves multiple factors. The current power system optimization dispatch method does not consider comprehensive factors, resulting in insufficient optimization of the dispatch strategy, which affects the stable operation and efficient power supply of the enterprise power system. Summary of the invention

[0004] The embodiments of the present invention provide a method, device, electronic device and storage medium for optimizing scheduling of an electric power system, which can comprehensively and accurately formulate an optimized scheduling strategy for the electric power system, improve the utilization rate of distributed power sources, and ensure the stable operation and efficient power supply of the electric power system.

[0005] In a first aspect, an embodiment of the present invention provides an optimization scheduling method for a power system, comprising:

[0006] Obtain historical operation data, power consumption data and current environmental data of the power system at the current moment;

[0007] Inputting the historical operation data, the power consumption data and the current environmental data into a pre-established optimization scheduling model, and obtaining the net present value of the power system according to the upper model of the optimization scheduling model;

[0008] The net present value is input into a lower model of the optimization scheduling model, an optimal scheduling strategy is obtained based on the lower model and the net present value, and the operation of the power system is controlled according to the optimal scheduling strategy.

[0009] In a second aspect, an embodiment of the present invention provides an optimization scheduling device for a power system, the device comprising:

[0010] A data acquisition module is used to acquire historical operation data, power consumption data and current environmental data of the power system at the current moment;

[0011] A data determination module, used for inputting the historical operation data, the power consumption data and the current environment data into a pre-established optimization scheduling model, and obtaining the net present value of the power system according to an upper model of the optimization scheduling model;

[0012] The optimal scheduling module is used to input the net present value into the lower model of the optimization scheduling model, obtain the optimal scheduling strategy based on the lower model and the net present value, and control the operation of the power system according to the optimal scheduling strategy.

[0013] In a third aspect, an embodiment of the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, an optimization scheduling method for an electric power system as described in any one of the embodiments of the present invention is implemented.

[0014] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for optimizing scheduling of a power system as described in any one of the embodiments of the present invention.

[0015] In a fifth aspect, an embodiment of the present invention provides a computer program product, including a computer program, which, when executed by a processor, implements the optimization scheduling method of the power system as described in any one of the embodiments of the present invention.

[0016] In an embodiment of the present invention, historical operation data, power consumption data and current environmental data of the power system are obtained; the historical operation data, power consumption data and current environmental data are input into a pre-established optimization scheduling model, and the net present value of the power system is obtained according to the upper model of the optimization scheduling model; the net present value is input into the lower model of the optimization scheduling model, and the optimal scheduling strategy is obtained based on the lower model and the net present value, and the operation of the power system is controlled according to the optimal scheduling strategy. The method of the embodiment of the present invention solves the optimal scheduling strategy according to the historical operation data, power consumption data and current environmental data at the current moment through the upper model and the lower model, ensures that the photovoltaic power generation meets the load demand while maximizing the energy efficiency, and helps the power system to allocate resources more effectively and improve energy utilization efficiency. At the same time, the method of the embodiment of the present invention has strong flexibility and scalability, and the model parameters and algorithms can be adjusted according to actual conditions to adapt to different application scenarios and needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 A first flow chart of an optimization dispatching method for a power system provided by an embodiment of the present invention;

[0019] Figure 2 A second flow chart of a method for optimizing scheduling of a power system provided by an embodiment of the present invention;

[0020] Figure 3 A schematic diagram of the structure of an optimization dispatching device for a power system provided by an embodiment of the present invention;

[0021] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0023] Figure 1 This is the first flow chart of a method for optimizing scheduling of an electric power system provided in an embodiment of the present invention. The method in the embodiment of the present invention can comprehensively and accurately formulate an optimized scheduling strategy for the electric power system, improve the utilization rate of distributed power sources, and ensure the stable operation and efficient power supply of the electric power system. The method can be executed by an optimized scheduling device for an electric power system provided in an embodiment of the present invention. The device can be implemented in software and / or hardware. The following embodiments will be described by taking the device integrated in an electronic device as an example. The electronic device can be a server or computer device for implementing a method for optimizing scheduling of an electric power system, etc. Figure 1 , the method may specifically include the following steps:

[0024] Step 101: Acquire historical operation data, power consumption data, and current environmental data of the power system at the current moment.

[0025] Among them, the power system can be a distributed photovoltaic power generation power system, which can not only provide power to the user side, but also generate additional power to power other external devices. Historical operation data is the operation data generated by the power system in the historical period. The specific duration of the historical period can be predetermined according to the field big data, such as one week or one month. Historical operation data includes historical load data, historical power generation data, and historical electricity price data. Historical load data includes the load situation of the power system in the historical period, that is, the power demand data in different time periods in the historical period. The historical load data can reflect the user's power consumption pattern and change trend. Historical power generation data includes the power generation of distributed photovoltaic power generation systems in different time periods. Historical power generation data helps to understand the power generation capacity and efficiency of photovoltaic systems, as well as their performance under different weather conditions. Historical electricity price data includes time-of-use electricity price information in historical periods. Power consumption data includes user power demand information, user power consumption habits and patterns, etc. Environmental data includes temperature, humidity, wind speed, cloud cover and sunshine intensity. Different environmental data will affect the power generation efficiency of photovoltaic panels and the load demand of the power system. For example, sunny weather is conducive to photovoltaic power generation, while rainy days may lead to a decrease in power generation. The necessary input information can be provided for the subsequent optimization dispatch model according to the historical data, power consumption data and environmental data of the power system. Therefore, when the power system needs to be optimized, the historical operation data, power consumption data and current environmental data of the power system at the current moment can be directly received through the pre-set data interface or directly received from the user.

[0026] Step 102: input the historical operation data, power consumption data and current environmental data into a pre-established optimization scheduling model, and obtain the net present value of the power system according to the upper model of the optimization scheduling model.

[0027] Among them, the optimization dispatch model is a tool for solving the optimal decision-making in complex systems or problems. The net present value reflects the time value of the benefits of the power system, that is, the value of a certain benefit at different time points is different. The optimization dispatch model in this scheme is used to determine the optimal dispatch strategy of the power system based on the historical operation data, power consumption data and environmental data of the power system. The optimal dispatch strategy includes parameters for reasonable planning and control of the power generation, transmission, distribution and power consumption of the power system. Before optimizing the dispatch of the power system, the optimization dispatch model can be pre-determined based on domain big data. For example, the optimization dispatch model is established using stochastic optimization methods and robust optimization methods, and the stacking optimization method handles the uncertainty of the output of wind farms and photovoltaic power stations. The robust optimization method is used to improve the stability and reliability of the model in an uncertain environment. The optimization dispatch model in this scheme is a two-layer model, and the upper model is used to predict and calculate the net present value of the power system in the future.

[0028] In an optional implementation, after obtaining historical operation data, power consumption data and environmental data, the historical load data, power consumption data and current environmental data are input into the load prediction module of the upper model, and the first sum is determined by the load prediction module according to the product of the predetermined temperature coefficient and the temperature, the product of the predetermined humidity coefficient and the humidity, the product of the predetermined illumination coefficient and the illumination, and the product of the predetermined date type coefficient and the date type; the predetermined basic load of the power system and the first sum are added to obtain the predicted load of the power system. The historical electricity price information and the current demand information are input into the electricity price prediction module of the upper model, the demand characteristics are determined according to the current demand information, and the seasonal characteristics are determined according to the historical operation data; the second sum is determined by the electricity price prediction module according to the product of the predetermined load relationship coefficient and the historical load, the product of the predetermined demand relationship coefficient and the demand characteristics, and the predetermined seasonal relationship coefficient and the seasonal characteristics; the predetermined basic electricity price and the second sum are added to obtain the user-side time-sharing predicted electricity price information. The predicted load and the user-side time-of-use predicted electricity price information are input into the energy efficiency calculation module of the upper model. Through the energy efficiency calculation module, the profit value of the power system is determined according to the predicted load and the user-side time-of-use predicted electricity price information; the net present value of the power system is calculated based on the profit value and a predetermined discount rate.

[0029] Step 103: input the net present value into the lower model of the optimization scheduling model, obtain the optimal scheduling strategy based on the lower model and the net present value, and control the operation of the power system according to the optimal scheduling strategy.

[0030] Among them, the lower model is used to solve and find the optimal dispatching strategy based on the output of the upper model to maximize the power generation efficiency of the distributed photovoltaic power generation power system under the premise of meeting certain constraints. The lower model can traverse all possible dispatching strategy combinations and evaluate the overall performance of each strategy. By comparing the performance of different strategies, the strategy that can maximize the overall efficiency is found as the optimal solution, that is, the optimal dispatching strategy.

[0031] The lower model in this scheme includes a constraint function, and the goal of the constraint function can be to maximize the benefits or efficiency of the power system. In an optional implementation, the electricity consumption data includes the power generation cost and power generation of the power system. After obtaining the net present value of the power system, the decision variables of the optimal scheduling strategy are determined through the lower model according to the power generation cost, power generation, the maximum output value obtained in advance and the constraint function; the optimal scheduling strategy is obtained according to the decision variables and the net present value. Further, after obtaining the optimal scheduling strategy, a scheduling instruction is generated according to the parameters in the optimal scheduling strategy, and the scheduling instruction is sent to each component of the corresponding power system (including photovoltaic panels, energy storage devices and smart meters, etc.), so that each component performs the corresponding operation according to the scheduling instruction. For example, the photovoltaic panel can adjust its power generation according to the instruction, the energy storage device can store or release energy according to the scheduling instruction, and the smart meter can monitor the real-time power consumption according to the scheduling instruction and perform the power purchase operation.

[0032] In this solution, the optimization scheduling model also includes a visualization module, which can display the optimal scheduling strategy to the user. The visualization module includes a real-time monitoring interface and data analysis tools. The visualization module can display the operating status of the distributed photovoltaic power generation power system, enterprise load data, and user-side time-of-use electricity price data, etc., which is convenient for users to monitor and analyze the operation of the distributed photovoltaic system.

[0033] The technical solution of this embodiment obtains the historical operation data, power consumption data and current environmental data of the power system at the current moment; inputs the historical operation data and environmental data into a pre-established optimization scheduling model, and obtains the net present value of the power system according to the upper model of the optimization scheduling model; inputs the net present value into the lower model of the optimization scheduling model, obtains the optimal scheduling strategy based on the lower model and the net present value, and controls the operation of the power system according to the optimal scheduling strategy. The technical solution of this embodiment solves the optimal scheduling strategy according to the historical operation data, power consumption data and current environmental data at the current moment through the upper model and the lower model, ensures that the photovoltaic power generation meets the load demand while maximizing the energy efficiency, and helps the power system to allocate resources more effectively and improve energy utilization efficiency. At the same time, the method of the embodiment of the present invention has strong flexibility and scalability, and the model parameters and algorithms can be adjusted according to actual conditions to adapt to different application scenarios and needs.

[0034] Figure 2 The second flow chart of the method for optimizing the dispatching of the power system provided in the embodiment of the present invention is a refinement of the above embodiment. The specific method can be as follows Figure 2 As shown, the method may include the following steps:

[0035] Step 201: Acquire historical operation data, power consumption data and current environmental data of the power system at the current moment.

[0036] Step 202: Input historical load data and current environmental data into the load prediction module of the upper model, and obtain the predicted load of the power system through the load prediction module.

[0037] Among them, the historical load data includes the load situation of the power system in the historical period, that is, the power demand data in different time periods in the historical period. The historical load data can reflect the user's power consumption pattern and change trend. The current environmental information includes temperature, humidity, light and date type, and the date type includes working days, rest days or statutory holidays. The upper model in this solution includes a load forecasting module, an electricity price forecasting module and an energy efficiency calculation module. The load forecasting module can predict the load on the user side for a period of time in the future based on historical load data and the current environment.

[0038] In this scheme, optionally, the predicted load of the power system is obtained through the load prediction module, including: determining the first sum through the load prediction module according to a predetermined temperature coefficient and the product of temperature, a predetermined humidity coefficient and the product of humidity, a predetermined light coefficient and the product of light, and a predetermined date type coefficient and the product of date type; adding the predetermined basic load of the power system and the first sum to obtain the predicted load of the power system.

[0039] Among them, the temperature coefficient represents the relationship between the user side load and temperature, the humidity coefficient represents the relationship between the user side load and humidity, the light coefficient represents the relationship between the user side load and the light intensity, and the date type coefficient represents the relationship between the user side load and the date type. Before optimizing the power system, the temperature coefficient, humidity coefficient, light coefficient and date type coefficient can be determined by linear fitting method based on a large amount of power system operation data and historical environmental data.

[0040] The first sum can be obtained by multiplying the predetermined temperature coefficient and the temperature, the predetermined humidity coefficient and the humidity, the predetermined illumination coefficient and the illumination, and the predetermined date type coefficient and the date type. For example, α1 represents the temperature coefficient, α2 represents the humidity coefficient, α3 represents the illumination coefficient, and α4 represents the date type coefficient.

[0041] The first sum is: α1T(t)+α2P(t)+α3H(t)+α4D(t), where T(t) represents the temperature at time t (the time period to be predicted), P(t) represents humidity, H(t) represents light intensity, and D(t) represents date type. The basic load is the minimum load of the power system without any external factors, that is, the minimum power load required by the power system under normal operating conditions. Add the basic load L0 of the power system to the first sum to obtain the predicted load L(t) of the power system: L(t)=L0+α1T(t)+α2P(t)+α3H(t)+α4D(t).

[0042] In the above steps, by comprehensively considering the impact of multiple factors such as temperature, humidity, light and date type on the load, the predetermined temperature coefficient, humidity coefficient, light coefficient and date type coefficient are multiplied with the corresponding environmental data to obtain the first sum, which is then added to the basic load, which can more accurately determine the actual load situation of the power system. The prediction method in the above steps can better capture the complexity and diversity of load changes than single factor prediction, thereby improving the accuracy of the prediction.

[0043] Step 203: input the historical electricity price information and the current demand information into the electricity price prediction module of the upper model, and obtain the user-side time-sharing predicted electricity price information through the electricity price prediction module.

[0044] Among them, the demand information includes information such as the power supply-demand ratio at the current moment. The historical electricity price information includes the time-of-use electricity price in the historical period. The electricity price prediction module is used to predict the user-side time-of-use electricity price in the future period based on the historical electricity price information and the current demand information. In this scheme, optionally, the user-side time-of-use predicted electricity price information is obtained through the electricity price prediction module, including: determining the demand characteristics according to the current demand information, and determining the seasonal characteristics according to the historical operation data; through the electricity price prediction module, the second sum is determined according to the product of the predetermined load relationship coefficient and the historical load, the product of the predetermined demand relationship coefficient and the demand characteristics, and the product of the predetermined seasonal relationship coefficient and the seasonal characteristics; the predetermined basic electricity price and the second sum are added to obtain the user-side time-of-use predicted electricity price information.

[0045] Among them, demand characteristics are used to assist the electricity price prediction module in predicting the trend of electricity prices changing with demand. Demand characteristics can be determined based on the power supply-demand ratio. According to historical operating data, seasonal characteristics are used to represent the relationship between electricity prices and seasons. For example, summer may be a peak period of electricity consumption, and electricity prices are relatively high; winter may be a low period of electricity consumption, and electricity prices are relatively low. The load relationship coefficient represents the relationship between electricity price and load, the demand relationship coefficient represents the relationship between load and demand, and the seasonal relationship coefficient represents the relationship between electricity price and season. Before optimizing the power system, the load relationship coefficient, demand relationship coefficient, and seasonal relationship coefficient can be determined by linear fitting methods based on a large amount of historical electricity price information, operating data, and historical environmental data.

[0046] The product of the load relationship coefficient and the historical load, the product of the demand relationship coefficient and the demand characteristics, and the seasonal relationship coefficient and the seasonal characteristics are added to obtain the second sum. Exemplarily, β1 represents the load relationship coefficient, β2 represents the load relationship coefficient, and β3 represents the load relationship coefficient. The second sum is: β1L(t)+β2M(t)+β3S(t); wherein L(t) represents the load, M(t) represents the demand characteristics, and S(t) represents the seasonal characteristics. The basic electricity price is a fixed electricity sales price uniformly set in advance. Add the basic electricity price E0 and the second sum to obtain the user-side time-sharing predicted electricity price information E(t): E(t)=E(t)+β1L(t)+β2M(t)+β3S(t).

[0047] In the above steps, the dynamic "second sum" is obtained by comprehensively considering the load relationship coefficient, demand relationship coefficient and seasonal relationship coefficient. The dynamic "second sum" is combined with the static basic electricity price, which not only takes into account the immediate changes in the market, but also maintains a certain degree of stability. This helps to make more accurate electricity price forecasts, thereby better planning electricity consumption strategies, reducing electricity costs, and improving the operating efficiency of the entire power system.

[0048] Step 204: input the predicted load and the user-side time-sharing predicted electricity price information into the energy efficiency calculation module of the upper model, and calculate the net present value of the power system through the energy efficiency calculation module.

[0049] The energy efficiency calculation module is used to calculate the revenue or energy efficiency of the distributed photovoltaic power generation power system in each time period according to the predicted load and predicted electricity price information. In this solution, optionally, the net present value of the power system is calculated by the energy efficiency calculation module, including: determining the revenue value of the power system according to the predicted load and the user-side time-sharing predicted electricity price information through the energy efficiency calculation module; calculating the net present value of the power system based on the revenue value and a predetermined discount rate.

[0050] Specifically, after obtaining the predicted load and the user-side time-sharing predicted electricity price information, multiply the predicted load and the user-side time-sharing predicted electricity price to obtain the revenue value of the power system. R(t) represents the revenue value at time t, then R(t) = E(t) × L(t). The net present value of the power system is determined by the following formula: Among them, NPV represents net present value, R(t) represents the return at time t, i represents the discount rate, and T represents the total time.

[0051] The energy efficiency calculation module can accurately evaluate the energy efficiency or benefits of the power system, providing an important basis for enterprises to optimize scheduling strategies.

[0052] Step 205: determine the decision variables of the optimal scheduling strategy according to the power generation cost, power generation, the maximum output value obtained in advance and the constraint function, and obtain the optimal scheduling strategy according to the decision variables and the net present value.

[0053] Specifically, the constraint function is a function predetermined based on domain big data and historical operation data of the power system, and the maximum output value is a value predetermined from the virtual power plant system, which represents the photovoltaic power generation output value. The decision variables of the optimal scheduling strategy are determined according to the following formula:

[0054]

[0055] Subject to G(t)≤B(t);

[0056] Among them, max means finding the maximum value, C(t) means the electricity price at time t, L(t) means the load, p(t) means the cost of photovoltaic power generation, G(t) means the photovoltaic power generation, and B(t) means the maximum output value. Subjectto G(t)≤B(t) means under the constraint of G(t)≤B(t). Furthermore, the decision variables of the optimal scheduling strategy are predicted based on the output data of the upper model, the relevant current environmental data and the constraint function, including the photovoltaic power generation and the charging and discharging status of the energy storage equipment in the optimal scheduling strategy. After obtaining the decision variables, the net present value and the net load value (the difference between the actual power consumption and the power generation of the distributed photovoltaic system) are comprehensively considered to formulate the optimal scheduling strategy.

[0057] In this solution, the optimal scheduling model also includes a communication control module, which is used to realize data exchange and command issuance between the upper model and the lower model. The communication control module includes a real-time data acquisition unit, a data transmission unit, and an instruction execution unit. The real-time data acquisition unit is used to collect photovoltaic operation data and load data; the data transmission unit is used to transmit real-time data to the upper model and the lower model; the instruction execution unit is used to receive the scheduling instructions of the upper model and convert them into specific control commands, which are issued to the distributed photovoltaic system and other power generation resources.

[0058] Step 206: Control the operation of the power system according to the optimal dispatching strategy.

[0059] For example, a company has a 5MW rooftop distributed photovoltaic power generation system, which is monitored and maintained by a virtual power plant system, which is connected to the power trading center platform. The company hopes to maximize the energy efficiency of its photovoltaic system. Through the upper-level model, the company's load in the next 24 hours is predicted based on the collected historical load data of the company and the current meteorological conditions, and it is determined that the company's load in the afternoon is higher. The user-side time-of-use electricity price for the next 24 hours is predicted based on historical electricity price data and current market conditions, and it is determined that the electricity price in the afternoon is higher. Further, the load forecast value in the afternoon minus the electricity price forecast value is determined to be the net load value. The optimal scheduling strategy determined based on the net load value and the lower-level model includes: the distributed photovoltaic system should be fully generated in the afternoon to maximize the energy efficiency of the power system.

[0060] In the technical solution of this embodiment, the historical operation data, power consumption data and current environmental data of the power system at the current moment are obtained. The historical load data and current environmental data are input into the load prediction module of the upper model, and the predicted load of the power system is obtained through the load prediction module. The historical electricity price information and the current demand information are input into the electricity price prediction module of the upper model, and the user-side time-sharing predicted electricity price information is obtained through the electricity price prediction module. The predicted load and the user-side time-sharing predicted electricity price information are input into the energy efficiency calculation module of the upper model, and the net present value of the power system is calculated through the energy efficiency calculation module. The decision variables of the optimal scheduling strategy are determined according to the power generation cost, power generation, the maximum output value obtained in advance and the constraint function. The optimal scheduling strategy is obtained according to the decision variables and the net present value. The operation of the power system is controlled according to the optimal scheduling strategy. The technical solution of this embodiment can obtain the predicted load of the power system through the load prediction module of the upper model, accurately determine the future load change trend, and provide a basis for formulating a reasonable scheduling strategy. The energy efficiency calculation module can accurately evaluate the energy efficiency or benefits of the power system, thereby accurately determining the optimal scheduling strategy, achieving effective allocation and utilization of power resources, improving the operating efficiency and economy of the power system, reducing operating costs, and ensuring the efficient and stable operation of the power system.

[0061] Figure 3 This is a schematic diagram of the structure of the power system optimization dispatching device provided in the embodiment of the present invention, which is suitable for executing the power system optimization dispatching method provided in the embodiment of the present invention. Figure 3 As shown, the device may specifically include:

[0062] The data acquisition module 301 is used to acquire the historical operation data, power consumption data and current environmental data of the power system at the current moment;

[0063] A data determination module 302 is used to input the historical operation data, the power consumption data and the current environment data into a pre-established optimization scheduling model, and obtain the net present value of the power system according to the upper model of the optimization scheduling model;

[0064] The optimal scheduling module 303 is used to input the net present value into the lower model of the optimization scheduling model, obtain the optimal scheduling strategy based on the lower model and the net present value, and control the operation of the power system according to the optimal scheduling strategy.

[0065] Optionally, the data determination module 302 is specifically used to: input the historical load data and the current environment data into the load prediction module of the upper model, and obtain the predicted load of the power system through the load prediction module;

[0066] Inputting the historical electricity price information and the current demand information into the electricity price prediction module of the upper model, and obtaining the user-side time-sharing predicted electricity price information through the electricity price prediction module;

[0067] The predicted load and the user-side time-sharing predicted electricity price information are input into the energy efficiency calculation module of the upper model, and the net present value of the power system is calculated by the energy efficiency calculation module.

[0068] Optionally, the data determination module 302 is further configured to: determine the first sum according to a predetermined temperature coefficient and a product of the temperature, a predetermined humidity coefficient and a product of the humidity, a predetermined illumination coefficient and a product of the illumination, and a predetermined date type coefficient and a product of the date type through the load prediction module;

[0069] A predetermined base load of the power system is added to the first sum to obtain a predicted load of the power system.

[0070] Optionally, the data determination module 302 is further used to: determine demand characteristics according to the current demand information, and determine seasonal characteristics according to the historical operation data;

[0071] Determining the second sum by the electricity price prediction module according to the product of a predetermined load relationship coefficient and a historical load, the product of a predetermined demand relationship coefficient and the demand characteristic, and the product of a predetermined season relationship coefficient and a season characteristic;

[0072] The predetermined basic electricity price and the second sum are added together to obtain the user-side time-sharing predicted electricity price information.

[0073] Optionally, the data determination module 302 is further used to: determine the revenue value of the power system according to the predicted load and the user-side time-sharing predicted electricity price information through the energy efficiency calculation module;

[0074] A net present value of the power system is calculated based on the benefit value and a predetermined discount rate.

[0075] Optionally, the optimal scheduling module 303 is specifically used to: determine the decision variables of the optimal scheduling strategy according to the power generation cost, the power generation, the pre-acquired maximum output value and the constraint function;

[0076] The optimal scheduling strategy is obtained according to the decision variables and the net present value.

[0077] The power system optimization dispatching device provided in the embodiment of the present invention can execute the power system optimization dispatching method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method. The contents not described in detail in this embodiment can refer to the description in any method embodiment of the present invention.

[0078] An embodiment of the present invention also provides a computer program product.

[0079] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer program products, which can include one or more computer programs, which can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor, which can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0080] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention, referring to Figure 4 , Figure 4 The electronic device 12 shown is only an example and should not limit the functions and scope of use of the embodiments of the present application. Figure 4As shown, the electronic device 12 is in the form of a general purpose computing device. The components of the electronic device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that connects various system components (including the system memory 28 and the processing unit 16).

[0081] Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures. By way of example, these architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MAC) bus, an Enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.

[0082] The electronic device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device 12, including volatile and non-volatile media, removable and non-removable media.

[0083] The system memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The electronic device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 may be used to read and write non-removable, non-volatile magnetic media ( Figure 4 not shown, usually called a "hard drive"). Although Figure 4 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical medium) may be provided. In these cases, each drive may be connected to the bus 18 via one or more data medium interfaces. The memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present application.

[0084] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in the memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. The program modules 42 generally perform the functions and / or methods of the embodiments described herein.

[0085] The electronic device 12 may also communicate with one or more external devices 14 (e.g., keyboards, pointing devices, displays 24, etc.), may communicate with one or more devices that enable a user to interact with the electronic device 12, and / or may communicate with any device that enables the electronic device 12 to communicate with one or more other computing devices (e.g., a network card, a modem, etc.). Such communication may be performed via an input / output (I / O) interface 22. Furthermore, the electronic device 12 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 20. As shown, the network adapter 20 communicates with other modules of the electronic device 12 via the bus 18. It should be understood that although Figure 4 Not shown, other hardware and / or software modules may be used in conjunction with the electronic device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0086] The processing unit 16 executes various functional applications and data processing by running the program stored in the system memory 28, for example, implementing an optimization scheduling method for an electric power system provided in an embodiment of the present invention: obtaining historical operation data, power consumption data and current environmental data of the electric power system at the current moment; inputting the historical operation data, the power consumption data and the current environmental data into a pre-established optimization scheduling model, and obtaining the net present value of the electric power system according to the upper model of the optimization scheduling model; inputting the net present value into the lower model of the optimization scheduling model, obtaining the optimal scheduling strategy based on the lower model and the net present value, and controlling the operation of the electric power system according to the optimal scheduling strategy.

[0087] The embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, an optimization scheduling method for a power system as provided in all the embodiments of the present invention is implemented: obtaining historical operation data, power consumption data and current environmental data of the power system at the current moment; inputting the historical operation data, the power consumption data and the current environmental data into a pre-established optimization scheduling model, and obtaining the net present value of the power system according to the upper model of the optimization scheduling model; inputting the net present value into the lower model of the optimization scheduling model, obtaining the optimal scheduling strategy based on the lower model and the net present value, and controlling the operation of the power system according to the optimal scheduling strategy. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electronic device, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection with one or more conductors, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction-executing electronic device, apparatus, or device.

[0088] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction-executing electronic device, apparatus, or device.

[0089] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0090] Computer program code for performing the operations of the present invention may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0091] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A method for optimizing the dispatching of a power system, characterized in that: The method comprises: Obtain historical operation data, power consumption data and current environmental data of the power system at the current moment; Inputting the historical operation data, the power consumption data and the current environmental data into a pre-established optimization scheduling model, and obtaining the net present value of the power system according to the upper model of the optimization scheduling model; The net present value is input into a lower model of the optimization scheduling model, an optimal scheduling strategy is obtained based on the lower model and the net present value, and the operation of the power system is controlled according to the optimal scheduling strategy.

2. The method according to claim 1, characterized in that The historical operation data includes historical load data, the electricity consumption data includes electricity price information and current demand information, and the net present value of the power system is obtained according to the upper model of the optimization scheduling model, including: Inputting the historical load data and the current environmental data into the load prediction module of the upper model, and obtaining the predicted load of the power system through the load prediction module; Inputting the historical electricity price information and the current demand information into the electricity price prediction module of the upper model, and obtaining the user-side time-sharing predicted electricity price information through the electricity price prediction module; The predicted load and the user-side time-sharing predicted electricity price information are input into the energy efficiency calculation module of the upper model, and the net present value of the power system is calculated by the energy efficiency calculation module.

3. The method according to claim 2, characterized in that The current environmental information includes temperature, humidity, light and date type, and the predicted load of the power system is obtained by the load prediction module, including: Determining, by the load prediction module, a first sum according to a predetermined temperature coefficient and a product of the temperature, a predetermined humidity coefficient and a product of the humidity, a predetermined illumination coefficient and a product of the illumination, and a predetermined date type coefficient and a product of the date type; A predetermined base load of the power system is added to the first sum to obtain a predicted load of the power system.

4. The method according to claim 2, characterized in that: The user-side time-sharing predicted electricity price information is obtained through the electricity price prediction module, including: Determine demand characteristics based on the current demand information, and determine seasonal characteristics based on the historical operation data; Determining the second sum by the electricity price prediction module according to the product of a predetermined load relationship coefficient and a historical load, the product of a predetermined demand relationship coefficient and the demand characteristic, and the product of a predetermined season relationship coefficient and a season characteristic; The predetermined basic electricity price and the second sum are added together to obtain the user-side time-sharing predicted electricity price information.

5. The method according to claim 2, characterized in that: Calculating the net present value of the power system by the energy efficiency calculation module includes: Determine the revenue value of the power system according to the predicted load and the user-side time-sharing predicted electricity price information through the energy efficiency calculation module; A net present value of the power system is calculated based on the benefit value and a predetermined discount rate.

6. The method according to claim 1, characterized in that The power consumption data includes the power generation cost and power generation of the power system, the lower layer model includes a constraint function, and an optimal dispatching strategy is obtained based on the lower layer model and the net present value, including: Determine the decision variables of the optimal scheduling strategy according to the power generation cost, power generation, the maximum output value obtained in advance and the constraint function; The optimal scheduling strategy is obtained according to the decision variables and the net present value.

7. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the computer program implements an optimization scheduling method for a power system according to any one of claims 1 to 6.

8. An optimization dispatching device for a power system, characterized in that: include: A data acquisition module is used to acquire historical operation data, power consumption data and current environmental data of the power system at the current moment; A data determination module, used for inputting the historical operation data, the power consumption data and the current environment data into a pre-established optimization scheduling model, and obtaining the net present value of the power system according to an upper model of the optimization scheduling model; The optimal scheduling module is used to input the net present value into the lower model of the optimization scheduling model, obtain the optimal scheduling strategy based on the lower model and the net present value, and control the operation of the power system according to the optimal scheduling strategy.

9. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the power system optimization scheduling method as described in any one of claims 1 to 6 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for optimizing scheduling of a power system as described in any one of claims 1 to 6 is implemented.

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