Integrated energy scheduling method and platform, storage medium and program product

Abstract

The invention discloses an integrated energy scheduling method and platform, a storage medium and a program product, and relates to the technical field of energy management, and the integrated energy scheduling method comprises the steps that an upper layer decision module obtains the energy information of each region, determines a first scheduling scheme based on the energy information and a preset upper layer cost target, and sends the first scheduling scheme to an upper layer decision module; the first scheduling scheme is sent to a lower-layer execution module, so that the lower-layer execution module modifies a current energy parameter based on the first scheduling scheme and a preset distribution method to obtain a first energy parameter, and the lower-layer execution module feeds back the first energy parameter to an upper-layer decision module; and the upper decision-making module performs scheme adjustment based on the feedback information to obtain a target scheduling scheme. Therefore, real-time changes of the market can be responded through real-time information transmission and adjustment of the upper layer and the lower layer, and optimal scheduling of multi-region comprehensive energy is achieved, so that the use benefits of the energy are improved.

Description

Technical Field

[0001] The present application relates to the field of energy management technology, and in particular to a comprehensive energy scheduling method, platform, storage medium and program product. Background Art

[0002] At present, renewable energy and multiple energy sources are usually mixed and configured in multiple areas of the energy system to form a multi-regional integrated energy system. And the management and operation of decentralized energy are optimized through virtual power plants. However, virtual power plants can usually only optimize the scheduling of a single energy source, resulting in low energy efficiency. Summary of the invention

[0003] The main purpose of this application is to provide a comprehensive energy scheduling method, platform, storage medium and program product, aiming to solve the technical problem that the current energy scheduling method is difficult to optimize the scheduling of multi-regional comprehensive energy based on real-time market changes during scheduling, resulting in low energy utilization efficiency.

[0004] To achieve the above objectives, the present application proposes a comprehensive energy scheduling method, which is applied to an upper-level decision module. The method includes:

[0005] Obtain energy information for each region;

[0006] Determining a first scheduling scheme based on the energy information and a preset upper-level cost target;

[0007] Sending the first scheduling scheme to a lower-layer execution module, so that the lower-layer execution module modifies the current energy parameter based on the first scheduling scheme and a preset allocation method, obtains a first energy parameter, and sends the first energy parameter to the local;

[0008] The first scheduling plan is adjusted based on the first energy parameter to obtain a target scheduling plan.

[0009] In one embodiment, the step of adjusting the first scheduling scheme based on the first energy parameter to obtain a target scheduling scheme includes:

[0010] determining a first energy cost based on the target scheduling scheme, wherein the target scheduling scheme includes the first scheduling scheme or the adjusted scheduling scheme;

[0011] Adjusting the target scheduling scheme based on the target energy parameter to obtain an adjusted scheduling scheme, wherein the target energy parameter includes a first energy parameter or a second energy parameter;

[0012] determining a second energy cost based on the adjusted scheduling scheme;

[0013] If the second energy cost is not equal to the first energy cost, sending the adjusted scheduling scheme to the lower-layer execution module, so that the lower-layer execution module modifies the current energy parameter based on the adjusted scheduling scheme and the preset allocation method to obtain the second energy parameter, and sending the second energy parameter to the local;

[0014] Return to the step of determining the first energy cost based on the target scheduling scheme until the second energy cost is equal to the first energy cost.

[0015] In one embodiment, the step of determining a first scheduling scheme based on the energy information and a preset upper-layer cost target includes:

[0016] Based on the energy information, determining a second scheduling scheme that can meet the preset energy net load;

[0017] Determining an upper-level energy scheduling cost based on the second scheduling scheme;

[0018] Based on the preset optimization algorithm, the optimized energy parameters are obtained with the goal of minimizing the upper energy scheduling cost;

[0019] Based on the optimized energy parameters, the second scheduling plan is modified to obtain the first scheduling plan.

[0020] In one embodiment, before the step of determining the second scheduling scheme and the upper-layer energy scheduling cost that can meet the preset energy net load based on the energy information, the step further includes:

[0021] Based on historical meteorological data, the electricity generated by renewable energy is predicted through a preset meteorological forecast model;

[0022] Based on historical load data and current load conditions, energy load is predicted through a preset load prediction model;

[0023] The preset energy net load is determined based on the electric energy generated by the renewable energy and the energy load.

[0024] To achieve the above objectives, the present application also proposes a comprehensive energy scheduling method, which is applied to the lower-level execution module, and the method includes:

[0025] Get current energy parameters;

[0026] The current energy parameter is modified based on the received first scheduling scheme and the preset allocation method to obtain the first energy parameter, and the first energy parameter is sent to the upper-level decision module so that the upper-level decision module can adjust the first scheduling scheme based on the first energy parameter to obtain the target scheduling scheme, wherein the first scheduling scheme is determined based on the energy information obtained by the upper-level decision module and the preset upper-level cost target.

[0027] In one embodiment, the step of modifying the current energy parameter based on the received first scheduling scheme and the preset allocation method to obtain the first energy parameter includes:

[0028] The first scheduling scheme and the current energy parameters are processed by a preset optimization model to obtain optimized energy parameters;

[0029] Modifying the current energy parameters into optimized energy parameters;

[0030] Based on the preset allocation method and the preset profit allocation target, the optimized energy parameter is modified to obtain the first energy parameter.

[0031] In one embodiment, the first scheduling scheme and the current energy parameters are processed by a preset optimization model to obtain optimized energy parameters, including:

[0032] Determine the power transaction cost and the total operation cost of the system based on the first dispatching scheme and the current energy parameters;

[0033] Obtaining operation benefits based on the electricity transaction cost and the total operation cost of the system;

[0034] Based on the operating benefits and preset operating benefit targets, the current energy parameters are modified through a preset optimization model to obtain optimized energy parameters.

[0035] In addition, to achieve the above objectives, the present application also proposes an integrated energy dispatching platform, which includes:

[0036] The upper-layer decision module obtains energy information of each area, determines a first scheduling scheme based on the energy information and a preset upper-layer cost target, and sends the first scheduling scheme to the lower-layer execution module, so that the lower-layer execution module modifies the current energy parameter based on the first scheduling scheme and a preset allocation method to obtain a first energy parameter, and sends the first energy parameter to the local area, adjusts the first scheduling scheme based on the first energy parameter, and obtains a target scheduling scheme;

[0037] The lower-level execution module obtains current energy parameters, modifies the current energy parameters based on the received first scheduling scheme and the preset allocation method to obtain first energy parameters, and sends the first energy parameters to the upper-level decision module so that the upper-level decision module can adjust the first scheduling scheme based on the first energy parameters to obtain a target scheduling scheme, wherein the first scheduling scheme is determined based on the energy information obtained by the upper-level decision module and the preset upper-level cost target.

[0038] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the comprehensive energy scheduling method described above are implemented.

[0039] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the comprehensive energy scheduling method described above.

[0040] One or more technical solutions proposed in this application have at least the following technical effects:

[0041] When it is necessary to schedule the multi-region integrated energy system, the upper-level decision module obtains the energy information of each region, determines the first scheduling scheme based on the energy information and the preset upper-level cost target, and sends the first scheduling scheme to the lower-level execution module; the lower-level execution module receives the first scheduling scheme and obtains the current energy parameters, modifies the current energy parameters based on the first scheduling scheme and the preset allocation method, obtains the first energy parameters, and sends the first energy parameters to the upper-level decision module; the upper-level decision module receives the first energy parameters, adjusts the first scheduling scheme based on the first energy parameters, and finally obtains the target scheduling scheme. Therefore, the present application can respond to real-time changes in the market through real-time information transmission and adjustment between the upper and lower layers, achieve optimal scheduling of multi-region integrated energy, and thus improve the efficiency of energy use. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0043] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0044] Figure 1 A flowchart of the first embodiment of the comprehensive energy scheduling method of the present application is provided;

[0045] Figure 2 This is a structural diagram of a single integrated energy system in the integrated energy scheduling method of this application

[0046] Figure 3 Flow chart for solving the two-layer collaborative model in the comprehensive energy dispatch method of this application

[0047] Figure 4 A flow chart of the second embodiment of the comprehensive energy scheduling method of the present application;

[0048] Figure 5 A flow chart of the third embodiment of the comprehensive energy scheduling method of the present application;

[0049] Figure 6 This is a collaborative optimization framework diagram of a virtual power plant including a multi-regional integrated energy system alliance in the integrated energy dispatch method of this application;

[0050] Figure 7 This is a flow chart of the fourth embodiment of the comprehensive energy scheduling method of this application.

[0051] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0052] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0053] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0054] Based on this, the embodiment of the present application provides a comprehensive energy scheduling method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the comprehensive energy scheduling method of the present application.

[0055] In this embodiment, the comprehensive energy scheduling method is applied to an upper-level decision module, and the method includes steps S10 to S40:

[0056] Step S10, obtaining energy information of each area;

[0057] It should be noted that the executor of this embodiment is the upper decision-making module in the integrated energy dispatching platform. The upper decision-making module is mainly used to formulate energy output, price and dispatching strategies based on the optimality of the overall system and market dynamics. In this embodiment, the upper decision-making module is the virtual power plant layer. The virtual power plant is not a physical entity, but through advanced information technology and control strategies, it centrally manages decentralized energy resources (such as wind power, solar energy, small hydropower and various backup generators), and optimizes the dispatch of these decentralized resources through smart grid technology, providing power supply and regulation services similar to traditional power plants.

[0058] In this embodiment, the main scenario that requires decentralized resource scheduling through the upper-level virtual power plant is energy scheduling in a multi-region integrated energy system. Multi-region means that energy needs to be scheduled between different regions, and each region has a different energy structure. The integrated energy system is composed of a power system, a thermal system, and a cooling system. These systems cover a variety of energy forms, such as electricity, natural gas, solar energy, and wind energy. Figure 2 , Figure 2 This is a structural diagram of a single integrated energy system.

[0059] Step S20, determining a first scheduling scheme based on the energy information and a preset upper-layer cost target;

[0060] The energy information obtained by the upper-level decision-making module includes market electricity prices, interactive power of the upper-level power grid, regional load demand and other information. The upper-level decision-making module determines the preliminary scheduling plan through the obtained energy information and the preset upper-level cost target. The energy cost information is mainly the sum of the cost information of all aspects of energy scheduling by the upper-level decision-making module, which serves as the goal of the upper-level decision-making module to determine the first scheduling plan.

[0061] The first dispatching scheme is a dispatching scheme that the upper-level decision module uses to achieve the preset upper-level cost target based on the acquired market electricity price, the interaction power of the upper-level power grid, the regional load demand and other information. The market electricity price is the price of electricity in the electricity market, the upper-level power grid interaction power is the amount of electric energy transmitted between power grids of different levels in the power system, and the regional load information is the amount of electricity demanded in the region. It can be understood that the first dispatching scheme is the initial dispatching scheme determined by the upper-level decision module.

[0062] The first dispatching plan includes specific dispatching strategies, the power generation of each unit in the integrated energy system, the electricity price of the integrated energy system alliance, the interactive power with the upper power grid and other information. The power generation of each unit is the power generation of the system that can generate electricity in the integrated energy system. The first dispatching plan is not the final dispatching plan. The upper-level decision-making module will also adjust the first dispatching plan based on the information feedback from the lower-level execution module and real-time information changes.

[0063] It can be understood that the upper-level decision-making module can obtain energy information such as market electricity prices, interactive power of the upper-level power grid, regional load demand, etc. The upper-level decision-making module sets specific scheduling strategies, the power generation power of each unit in the integrated energy system, the electricity price of the integrated energy system alliance, and the interactive power with the upper-level power grid based on the energy information obtained. Since the set information will affect the cost of energy scheduling, the energy cost is used as the judgment standard to determine the scheduling plan that achieves the preset upper-level cost target as the first scheduling plan.

[0064] In a feasible implementation manner, based on the energy information and the preset upper-layer cost target, the step of determining the first scheduling scheme may also be:

[0065] Based on the energy information, determine a second scheduling scheme that can meet the preset energy net load; based on the second scheduling scheme, determine the upper-level energy scheduling cost; based on a preset optimization algorithm, obtain optimized energy parameters with the goal of minimizing the upper-level energy scheduling cost; based on the optimized energy parameters, modify the second scheduling scheme to obtain the first scheduling scheme.

[0066] It should be noted that the preset net energy load refers to the residual load after deducting the power generation of renewable energy (such as wind power, solar energy, etc.) from the actual electricity demand in a power system or power grid within a given time. The use of the preset net energy load is mainly due to the uncertainty and volatility of renewable energy. The output of renewable energy (such as wind power and photovoltaic power generation) varies with weather, time and other conditions, and is not easy to predict stably. Therefore, usually only the net load is considered, that is, the part that cannot be met by renewable energy. At the same time, because the marginal cost of renewable energy is close to zero, because it does not rely on fuel costs (such as wind power and solar energy), the cost of power generation is low, so the cost of power generation from renewable energy is usually not included.

[0067] The second scheduling plan is a scheduling plan determined by the upper decision-making system to meet the preset energy net load. Although this scheduling plan meets the net load of energy, it does not necessarily minimize the upper energy scheduling cost. Therefore, it is necessary to adjust the second scheduling plan through a preset algorithm. The upper energy scheduling cost is composed of the cost of the upper power grid interaction power, the cost of the interaction power with the lower integrated energy system, and the cost of the generator set output, that is, it is composed of the upper power grid interaction power, the interaction power with the lower integrated energy system, and the cost of the generator set output in the second scheduling plan. The specific expression is:

[0068]

[0069] is the cost of the upper grid interaction power, C IES(t) is the cost of interacting with the underlying integrated energy system, Therefore, in order to achieve the goal of minimizing the upper energy scheduling cost, it is necessary to obtain the above parameters in the second scheduling scheme through a preset optimization algorithm.

[0070] The preset optimization model is an algorithm model that can solve complex optimization problems. The metaheuristic algorithm, as an advanced search algorithm for solving complex optimization problems, can be applied to such complex optimization problems. For example, the dung beetle optimization algorithm, genetic algorithm, simulated annealing algorithm, particle swarm algorithm and other algorithms can be used to solve the above parameters. The parameters obtained by solving can minimize the energy scheduling cost of the upper layer. Therefore, the first scheduling scheme is obtained by modifying the above parameters in the second scheduling scheme to the parameters obtained by solving.

[0071] In a feasible embodiment, before the step of determining the second scheduling scheme and the upper-layer energy scheduling cost that can meet the preset energy net load based on the energy information, the following steps may also be performed:

[0072] Based on historical meteorological data, the amount of electric energy generated by renewable energy is predicted through a preset meteorological prediction model; based on historical load data and current load conditions, the energy load is predicted through a preset load prediction model; based on the electric energy generated by the renewable energy and the energy load, the preset energy net load is determined.

[0073] It should be noted that the meteorological forecasting model is used to combine historical meteorological data to calculate the changes of various variables in the atmosphere (such as temperature, humidity, wind speed, etc.) over time and space, and the load forecasting model is used to combine historical load data and current load conditions to predict energy load.

[0074] It is understandable that the preset net energy load is obtained by subtracting the energy load that renewable energy can provide from the current energy load. Since renewable energy is a highly uncertain energy source such as wind energy and solar energy, renewable energy can be predicted by combining a meteorological forecast model with historical meteorological data. Similarly, energy load can be predicted by combining a load forecast model with historical load data and current load conditions. The net energy load can be obtained by combining the predicted energy load and the amount of electricity generated by renewable energy.

[0075] Step S30, sending the first scheduling scheme to a lower-layer execution module, so that the lower-layer execution module modifies the current energy parameter based on the first scheduling scheme and a preset allocation method, obtains a first energy parameter, and sends the first energy parameter to the local;

[0076] The current energy parameters obtained by the lower-level execution module are mainly data such as the power generation power of each device, the power of user demand response, the power of energy interaction, and the power purchase and sale strategy, which can be set as parameters. The equipment of the lower-level execution module mainly includes gas turbines, batteries, solar power generation, wind power generation, power-to-gas equipment, heat storage tanks, waste heat boilers, gas boilers and other equipment.

[0077] The power generation capacity of each device is the power generation capacity of each device in the generator set based on the determined power generation capacity of each generator set. The user demand response power is the specific adjustment quantity that optimizes the operation of the entire system by guiding the user's behavior through price under specific circumstances. The energy interaction power refers to the specific amount of electricity exchange between the lower-level integrated energy systems. The power purchase and sales strategy refers to the amount of electricity purchased and sold with the upper-level decision-making module.

[0078] It can be understood that after determining the first scheduling plan, the upper-level decision-making module will send the first scheduling plan to the lower-level execution module. The lower-level execution module will receive the power generation information of each unit in the integrated energy system, and optimize the output power of the specific equipment in each unit based on this information; it will receive the electricity price information of the integrated energy system alliance, and modify the specific power purchase and sales strategy based on this information; it will receive the scheduling strategy and the interaction power with the upper-level power grid and other information, and based on this information, it will modify parameters such as user demand response power and energy interaction power between integrated energy system alliances to obtain modified energy parameters.

[0079] After obtaining the modified energy parameters, the integrated energy system alliance will also optimize and adjust the output of the integrated energy system equipment of each device in the alliance and the power purchase and sales strategy with the virtual power plant based on the preset allocation method, so that the determined parameters can achieve the preset optimization goals. After the lower-level execution module determines the first energy parameter, the first energy parameter is fed back to the upper-level decision module, so that the upper-level decision module can modify the first scheduling plan according to the first energy parameter. Figure 3 , Figure 3 Solve the flow chart for the two-layer collaborative model.

[0080] Step S40: adjusting the first scheduling plan based on the first energy parameter to obtain a target scheduling plan.

[0081] It should be noted that the first energy parameter received by the upper-level decision module includes data such as the power generation power of each device, the user demand response power, the energy interaction power, and the power purchase and sale strategy. Since these data will affect the cost of energy scheduling by the upper-level decision module, the upper-level decision module needs the current scheduling plan to be unable to achieve the preset upper-level cost target. Therefore, the upper-level decision module needs to adjust the current scheduling plan so that the adjusted target scheduling plan can achieve the preset upper-level cost target. The above process of adjusting the current scheduling plan to obtain the target scheduling plan is not obtained once, but is obtained after multiple iterative updates. If the energy information (regional load, market demand, etc.) changes, the upper-level decision module will also adjust the scheduling plan to obtain the final target scheduling plan.

[0082] In summary, when it is necessary to schedule the multi-regional integrated energy system, the upper-level decision module predicts energy information such as the energy load and renewable energy amount of each region, calculates the net energy load, determines the second scheduling scheme that can meet the preset net energy load, and modifies the second scheduling scheme through the preset optimization model and cost target to obtain the first scheduling scheme. The upper-level decision module sends the first scheduling scheme to the lower-level execution module; the lower-level execution module receives the first scheduling scheme and obtains the current energy parameters, modifies the current energy parameters based on the first scheduling scheme and the preset allocation method, obtains the first energy parameters, and sends the first energy parameters to the upper-level decision module; the upper-level decision module receives the first energy parameters, adjusts the first scheduling scheme based on the first energy parameters, and finally obtains the target scheduling scheme through multiple iterative adjustments. Therefore, the present application can respond to real-time changes in the market through real-time information transmission and adjustment between the upper and lower layers, and achieves optimized scheduling of multi-regional integrated energy, thereby improving the efficiency of energy use.

[0083] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can refer to the above introduction, and will not be repeated later. Figure 4 , Figure 3 The flowchart of the second embodiment of the comprehensive energy scheduling method of the present application, step S30, sending the first scheduling scheme to the lower-level execution module, so that the lower-level execution module modifies the current energy parameter based on the first scheduling scheme and the preset allocation method, obtains the first energy parameter, and sends the first energy parameter to the local, including steps S31 to S35:

[0084] Step S31, determining a first energy cost based on the target scheduling scheme, wherein the target scheduling scheme is the first scheduling scheme or an adjusted scheduling scheme;

[0085] It should be noted that the target scheduling plan can be the first scheduling plan, or it can be an adjusted scheduling plan adjusted on the basis of the first scheduling plan. The first energy cost is the operating cost of the upper layer of the comprehensive energy scheduling system when performing energy scheduling. This energy cost is determined based on the first scheduling plan or the adjusted scheduling plan.

[0086] The operating cost of the upper layer when performing energy scheduling is composed of the cost of the upper power grid interaction power, the cost of the interaction power with the lower integrated energy system, and the cost of the generator set output. The cost of the upper power grid interaction power is determined by the interaction power of the upper power grid, the cost of the interaction power with the lower integrated energy system is determined by the electricity price strategy formulated for the integrated energy system alliance, and the cost of the generator set output is determined by the output power of the generator set. Based on these parameters in the first scheduling scheme, the first energy cost of the first scheduling scheme can be determined.

[0087] Step S32, adjusting the target scheduling scheme based on the target energy parameter to obtain an adjusted scheduling scheme, where the target energy parameter includes a first energy parameter or a second energy parameter;

[0088] It should be noted that the adjusted scheduling plan is obtained by adjusting the target scheduling plan based on the target energy parameters, and the target scheduling plan can be the first scheduling plan or the adjusted scheduling plan, that is, the adjusted scheduling plan can be a scheduling plan obtained by adjusting the initial scheduling plan one or more times.

[0089] Step S33, determining a second energy cost based on the adjusted scheduling scheme;

[0090] It should be noted that the first energy cost is the energy cost obtained before the upper decision module modifies the plan, and the second energy cost is the energy cost of the adjusted scheduling plan obtained by the upper decision module modifying the target scheduling plan after feedback from the lower execution module. The first energy cost and the second energy cost are determined according to the interaction power of the upper power grid, the interaction power with the lower integrated energy system, and the output power of the generator set in the scheduling plan before and after the adjustment, respectively.

[0091] Step S34, if the second energy cost is not equal to the first energy cost, sending the adjusted scheduling scheme to the lower-layer execution module, so that the lower-layer execution module modifies the current energy parameter based on the adjusted scheduling scheme and the preset allocation method, obtains the second energy parameter, and sends the second energy parameter to the local;

[0092] It can be understood that since the energy cost is related to the interaction power of the upper power grid determined by the upper decision-making module, the interaction power with the lower integrated energy system and the output power of the generator set, and the upper virtual power plant will modify the interaction power of the upper power grid, the interaction power with the lower integrated energy system and the output power of the generator set based on the feedback from the lower execution module, the first energy cost and the second energy cost will be different when the scheduling plan needs to be modified.

[0093] When the first energy cost is different from the second energy cost, it means that the upper-level decision-making module has modified the target scheduling plan to achieve the preset upper-level cost target. The integrated energy scheduling system needs to send the adjusted scheduling plan modified by the upper-level decision module to the lower-level execution module, so that the lower-level execution module can modify the current energy parameters based on the adjusted scheduling plan and the preset allocation method, obtain the second energy parameters, and send the second energy parameters to the local.

[0094] Step S35, returning to the step of determining the first energy cost based on the target scheduling scheme until the second energy cost is equal to the first energy cost.

[0095] It can be understood that when the first energy cost is different from the second energy cost, it means that the energy parameters such as the interaction power of the upper power grid, the interaction power with the lower integrated energy system and the output power of the generator set in the scheduling plan before modification and the scheduling plan after modification have been modified by the upper-level decision-making module. Therefore, the integrated energy scheduling system needs to re-perform information interaction between the upper and lower layers, calculation of the first energy cost, modification of the scheduling plan, modification of the second energy cost, comparison of the first and second energy costs, and other steps until the first energy cost is the same as the second energy cost.

[0096] When the first energy cost is the same as the second energy cost, it means that after the upper-level decision module modifies the first scheduling plan, the parameters such as the interaction power of the upper power grid, the interaction power with the lower-level integrated energy system, and the output power of the generator set have not changed. It means that after receiving the feedback information from the lower-level execution module, the upper-level decision module does not need to change the parameters such as the interaction power of the upper power grid, the interaction power with the lower-level integrated energy system, and the output power of the generator set in the first scheduling plan, and can also achieve the preset upper-level cost target. Therefore, the integrated energy scheduling system has reached the optimal operating state, the iterative process can be terminated, and the current scheduling plan at this time is used as the target scheduling plan.

[0097] Specifically, the upper decision module determines a scheduling scheme No. 1, sets the parameters such as the interaction power of the upper power grid, the interaction power with the lower integrated energy system, and the output power of the generator set, and uses it as the first scheduling scheme, which is the target scheduling scheme. The upper decision module calculates the energy cost No. 1 according to the above parameters and uses it as the first energy cost. The upper decision module sends the scheduling scheme No. 1 to the lower execution module, and then receives the feedback information from the lower execution module. The upper decision module modifies the scheduling scheme No. 1 according to the feedback information to obtain the scheduling scheme No. 2, which is the adjusted scheduling scheme, and determines the energy cost No. 2 according to the parameters such as the interaction power of the upper power grid, the interaction power with the lower integrated energy system, and the output power of the generator set in the scheduling scheme No. 2, which is the second energy cost.

[0098] The upper decision module compares the second energy cost with the first energy cost, and the second energy cost is different from the first energy cost. The No. 2 scheduling plan is sent to the lower execution module, and then, after receiving the feedback information of the lower execution module, the upper decision module modifies the No. 2 scheduling plan according to the feedback information to obtain the No. 3 scheduling plan. At this time, the No. 2 scheduling plan is the target scheduling plan, and the No. 3 scheduling plan is the adjusted scheduling plan. The No. 3 energy cost is determined according to the parameters such as the interaction power of the upper power grid in the No. 3 scheduling plan, the interaction power with the lower comprehensive energy system, and the output power of the generator set. This energy cost is the second energy cost.

[0099] The upper decision module compares the second energy cost with the first energy cost, that is, compares the No. 3 energy cost with the No. 2 energy cost, and the second energy cost is equal to the first energy cost. This means that after the upper decision module receives the feedback information from the lower execution module, the upper decision module does not modify the parameters such as the interaction power of the upper power grid, the interaction power with the lower integrated energy system, and the output power of the generator set. Therefore, the integrated energy dispatching system has been able to achieve the best operating state, and the No. 3 dispatching plan is the target dispatching plan.

[0100] In summary, when it is necessary to dispatch a multi-regional integrated energy system, this embodiment performs information interaction between the upper and lower layers, and modifies the dispatching scheme and energy parameters based on the interactive information. Through such an iterative optimization method, the integrated energy dispatching system can modify some parameters in the dispatching scheme such as electricity prices in real time according to changes in energy parameters such as regional load and market demand. The energy cost is calculated based on information such as the interactive power of the upper power grid in the dispatching scheme, the interactive power with the lower integrated energy system, and the output power of the generator set. Whether the energy cost changes before and after the scheme is modified is used to determine whether the system has reached the optimal operating state. Therefore, this embodiment can dynamically formulate electricity prices based on information such as regional load and market demand, optimize resource allocation, and improve energy efficiency and economy. At the same time, this dynamic pricing enables the power interaction between the integrated energy system and the virtual power plant to play a significant role in peak shaving and valley filling of regional loads.

[0101] Based on this, the third embodiment of the present application also provides a comprehensive energy scheduling method, referring to Figure 5 , Figure 5 This is a flow chart of the third embodiment of the comprehensive energy scheduling method of the present application; the same or similar contents as those of the above-mentioned embodiments 1 and 2 can be referred to the above introduction and will not be repeated later.

[0102] The comprehensive energy scheduling method is applied to the lower-level execution module, and the method includes steps H10 to H20:

[0103] Step H10, obtaining current energy parameters;

[0104] It should be noted that the execution subject of this embodiment is the lower-layer execution module in the integrated energy scheduling platform, and the lower layer obtains the current energy parameters to modify the energy parameters according to the received scheduling plan.

[0105] Step H20, modify the current energy parameters based on the received first scheduling scheme and the preset allocation method to obtain the first energy parameters, and send the first energy parameters to the upper-level decision module, so that the upper-level decision module can adjust the first scheduling scheme based on the first energy parameters to obtain the target scheduling scheme, wherein the first scheduling scheme is determined based on the energy information obtained by the upper-level decision module and the preset upper-level cost target.

[0106] It should be noted that the lower-level execution module mainly integrates various forms of energy by establishing the linkage and interaction of multiple regions, coupling the energy conversion equipment of multiple regions, meeting the load requirements of multiple regions, achieving mutual complementarity between regions, and further improving the economic benefits of the entire system. Integrated energy is an energy system that includes multiple regions and multiple energy forms. It is mainly a comprehensive energy system that mixes multiple energy sources such as electricity, natural gas, solar energy and wind energy in multiple regions. It is an alliance system composed of multiple of the above-mentioned comprehensive energy systems.

[0107] It is understandable that the lower-level integrated energy system alliance can interact with the virtual power plant through the electricity price strategy formulated by the upper-level virtual power plant, and the lower-level integrated energy system alliances can also interact with each other. Figure 6 , Figure 6 The figure is a collaborative optimization framework diagram of a virtual power plant with a multi-regional integrated energy system alliance. Therefore, the interaction between the lower integrated energy system alliance and the upper virtual power plant and the lower integrated energy system alliance can be optimized by optimizing the current energy parameters.

[0108] The lower-level execution module modifies the current energy parameter based on the received first scheduling scheme and the preset allocation method to obtain the first energy parameter, and sends the first energy parameter to the upper-level decision module for the upper-level decision module to optimize the scheduling scheme, and sends it to the lower-level execution module for further optimization.

[0109] In summary, the lower-level execution module obtains parameters such as the power generation of each device, the user demand response power, the energy interaction power, the power purchase and sale strategy, and changes the above parameters based on the received scheduling plan to optimize the interaction effect between the lower-level integrated energy system alliance and the upper-level virtual power plant and the lower-level integrated energy system alliance. The modified parameters are then sent to the upper-level decision module for the upper-level decision module to optimize the scheduling plan, and sent to the lower-level execution module for further optimization. Therefore, this embodiment can optimize some energy parameters through the lower-level execution module, and send the optimized parameters to the upper layer for the next optimization, so as to achieve better energy scheduling effects and improve the efficiency of energy use.

[0110] Based on the first to third embodiments of the present application, in the fourth embodiment of the present application, the same or similar contents as those of the first to third embodiments can be referred to the above description, and will not be described in detail later. Figure 7 , Figure 7 The flowchart of the fourth embodiment of the comprehensive energy scheduling method of the present application, step H20, the current energy parameter is modified based on the received first scheduling scheme and the preset allocation method to obtain the first energy parameter, including steps H21 to H23:

[0111] Step H21, processing the first scheduling scheme and the current energy parameters through a preset optimization model to obtain optimized energy parameters;

[0112] It should be noted that the preset optimization model is used to modify the current energy parameters obtained by the lower-level execution module so that the lower-level execution module can participate in the energy scheduling process with the optimal energy parameters. The preset optimization model can be a variety of models, but it needs to be an optimization model that can better handle multi-dimensional and complex optimization problems. This embodiment uses a 0-1 mixed integer linear programming model as the preset optimization model.

[0113] Mixed integer linear programming is a mathematical optimization model that is suitable for solving linear programming problems that contain both continuous variables and integer variables. The 0-1 mixed integer linear programming model is used to solve problems where some decision variables are integers. "0-1" means that some variables can only take two values, 0 or 1. In this embodiment, some decision variables are binary variables in 0 and 1 states, such as some switch variables that only have two states, on and off, and some decisions that only have two states, whether the device is on or off. Therefore, the 0-1 mixed integer linear programming model can better solve the underlying energy scheduling optimization problem.

[0114] It can be understood that the lower-level execution module obtains parameter information such as the interaction power of the upper-level power grid, the interaction power with the lower-level integrated energy system, and the output power of the generator set by receiving the first scheduling plan sent by the upper-level decision-making module. The above information will affect the current energy parameters such as the power generation power of each device, the user's demand response power, the energy interaction power, and the power purchase and sales strategy. The lower-level execution module solves the optimal current energy parameters mentioned above through a 0-1 mixed integer linear programming model.

[0115] Step H22, modifying the current energy parameters to optimized energy parameters;

[0116] It can be understood that the lower-level execution module optimizes and solves parameters such as the power generation power of each device, the user's demand response power, the energy interaction power, and the power purchase and sales strategy through a 0-1 mixed integer linear programming model to obtain the optimized energy parameters, and then optimizes the energy scheduling efficiency of the lower-level integrated energy system alliance layer by modifying the energy parameters to the solved energy parameters.

[0117] Step H23: based on the preset allocation method and the preset profit allocation target, modify the optimized energy parameter to obtain the first energy parameter.

[0118] It should be noted that the preset allocation method is used to optimize the energy parameters after solving the 0-1 mixed integer linear programming model by allocation to obtain the final target energy parameters. The allocation method used can be various allocation methods that can achieve the optimization goal. In this embodiment, the lower-level execution module is distributed profit by the Shapley value profit distribution method to optimize the energy parameters. The Shapley value allocation method is an allocation method used in cooperative game theory to determine the contribution of each participant in the alliance. The profit is mainly distributed according to the marginal contribution of each participant to the total revenue of the alliance. In this embodiment, each participant is the various integrated energy systems in the integrated energy system alliance. The parts that need to be allocated are mainly the parameters such as the electricity price and power generation of each integrated energy system.

[0119] It can be understood that the lower-level execution module optimizes the current energy parameters through the 0-1 mixed integer linear programming model. Since the various integrated energy systems will interact with each other in terms of electric energy, the energy parameters are also optimized based on the interaction between the various integrated energy systems so that the lower-level execution module can achieve the highest operating efficiency. Therefore, the profit can be distributed based on the marginal contribution of each integrated energy system to the total revenue of the lower-level execution module through the Shapley value allocation method.

[0120] In a feasible implementation manner, step H21, processing the first scheduling scheme and the current energy parameters through a preset optimization model to obtain an optimized energy parameter step, may also be:

[0121] Based on the first scheduling scheme and the current energy parameters, the electricity transaction cost and the total operating cost of the system are determined; based on the electricity transaction cost and the total operating cost of the system, the operating benefit is obtained; based on the operating benefit and the preset operating benefit target, the current energy parameters are modified through a preset optimization model to obtain optimized energy parameters.

[0122] It should be noted that the power transaction cost is the transaction cost of electricity between the virtual power plant and each integrated energy system, which is determined by the electricity price strategy formulated by the upper decision module for the lower execution module. The total cost of system operation is obtained by adding the costs of the power system, thermal system and weather system. The lower execution module obtains the expression of operating benefits based on the power transaction cost and the total operating cost of the system:

[0123]

[0124] is the total operating cost of the power system, is the total operating cost of the thermal system, is the total operating cost of the natural gas system, C IES(t) is the electricity transaction cost between the virtual power plant and various integrated energy systems.

[0125] It can be understood that the electricity transaction between the virtual power plant and various integrated energy systems is actually to provide electricity to the integrated energy system alliance at the lower level. Therefore, when calculating the operating benefits, it is necessary to make the difference between the electricity transaction cost between the virtual power plant and various integrated energy systems and the total operating cost of the system as large as possible.

[0126] In this embodiment, t is the operation time of the lower integrated energy system alliance, T is 24 hours, and n is 3, indicating that the number of lower integrated energy systems of the lower integrated energy system alliance is 3. Therefore, the operation benefit is the difference between the electricity transaction cost between the virtual power plant and each integrated energy system and the total operation cost of the three lower integrated energy systems for 24 hours.

[0127] In summary, this embodiment calculates the operating benefit by the difference between the electricity transaction cost between the virtual power plant and each integrated energy system and the total operating cost of the system, and solves the energy parameters through a preset optimization model to maximize the operating benefit of the lower layer. Therefore, this embodiment optimizes the energy parameters with the goal of maximizing the operating benefit to obtain better energy parameters, thereby improving the efficiency of energy use.

[0128] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the comprehensive energy scheduling method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0129] The present application provides a comprehensive energy dispatching platform, which includes:

[0130] The upper-layer decision module obtains energy information of each area, determines a first scheduling scheme based on the energy information and a preset upper-layer cost target, and sends the first scheduling scheme to the lower-layer execution module, so that the lower-layer execution module modifies the current energy parameter based on the first scheduling scheme and a preset allocation method to obtain a first energy parameter, and sends the first energy parameter to the local area, adjusts the first scheduling scheme based on the first energy parameter, and obtains a target scheduling scheme;

[0131] The lower-level execution module obtains current energy parameters, modifies the current energy parameters based on the received first scheduling scheme and the preset allocation method to obtain first energy parameters, and sends the first energy parameters to the upper-level decision module so that the upper-level decision module can adjust the first scheduling scheme based on the first energy parameters to obtain a target scheduling scheme, wherein the first scheduling scheme is determined based on the energy information obtained by the upper-level decision module and the preset upper-level cost target.

[0132] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the comprehensive energy scheduling method in the above-mentioned embodiment.

[0133] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, 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 embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0134] The computer-readable storage medium may be included in the integrated energy device; or may exist independently without being assembled into the integrated energy device.

[0135] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the integrated energy device, the integrated energy device executes the integrated energy scheduling method.

[0136] Computer program code for performing the operations of the present application 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).

[0137] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to each embodiment of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0138] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0139] The readable storage medium provided by the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned integrated energy scheduling method, and can solve the technical problem that the current energy scheduling method is difficult to optimize the scheduling of multi-region integrated energy based on real-time changes in the market during scheduling, resulting in low energy utilization efficiency. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as the beneficial effects of the integrated energy scheduling method provided by the above-mentioned embodiment, and will not be repeated here.

[0140] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned comprehensive energy scheduling method when executed by a processor.

[0141] The computer program product provided by the present application can solve the technical problem that the current energy scheduling method is difficult to optimize the scheduling of multi-regional integrated energy based on real-time changes in the market during scheduling, resulting in low energy utilization efficiency. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as the beneficial effects of the integrated energy scheduling method provided by the above embodiment, and will not be repeated here.

[0142] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A comprehensive energy scheduling method, characterized in that: Applied to the upper decision module, the method includes: Obtain energy information for each region; Determining a first scheduling scheme based on the energy information and a preset upper-level cost target; Sending the first scheduling scheme to a lower-layer execution module, so that the lower-layer execution module modifies the current energy parameter based on the first scheduling scheme and a preset allocation method, obtains a first energy parameter, and sends the first energy parameter to the local; The first scheduling plan is adjusted based on the first energy parameter to obtain a target scheduling plan.

2. The method according to claim 1, characterized in that The step of adjusting the first scheduling scheme based on the first energy parameter to obtain a target scheduling scheme includes: Determining a first energy cost based on the target scheduling scheme, wherein the target scheduling scheme is the first scheduling scheme or the adjusted scheduling scheme; Adjusting the target scheduling scheme based on the target energy parameter to obtain an adjusted scheduling scheme, wherein the target energy parameter includes a first energy parameter or a second energy parameter; determining a second energy cost based on the adjusted scheduling scheme; If the second energy cost is not equal to the first energy cost, sending the adjusted scheduling scheme to the lower-layer execution module, so that the lower-layer execution module modifies the current energy parameter based on the adjusted scheduling scheme and the preset allocation method to obtain the second energy parameter, and sending the second energy parameter to the local; Return to the step of determining the first energy cost based on the target scheduling scheme until the second energy cost is equal to the first energy cost.

3. The method according to claim 1, characterized in that: The step of determining a first scheduling scheme based on the energy information and a preset upper-layer cost target includes: Based on the energy information, determining a second scheduling scheme that can meet the preset energy net load; Determining an upper-level energy scheduling cost based on the second scheduling scheme; Based on the preset optimization algorithm, the optimized energy parameters are obtained with the goal of minimizing the upper energy scheduling cost; Based on the optimized energy parameters, the second scheduling plan is modified to obtain the first scheduling plan.

4. The method according to claim 3, characterized in that: Before the step of determining the second scheduling scheme and the upper-layer energy scheduling cost that can meet the preset energy net load based on the energy information, the step further includes: Based on historical meteorological data, the electricity generated by renewable energy is predicted through a preset meteorological forecast model; Based on historical load data and current load conditions, energy load is predicted through a preset load prediction model; The preset energy net load is determined based on the electric energy generated by the renewable energy and the energy load.

5. A comprehensive energy scheduling method, characterized in that: Applied to the lower layer execution module, the method described includes: Get current energy parameters; The current energy parameter is modified based on the received first scheduling scheme and the preset allocation method to obtain the first energy parameter, and the first energy parameter is sent to the upper-level decision module so that the upper-level decision module can adjust the first scheduling scheme based on the first energy parameter to obtain the target scheduling scheme, wherein the first scheduling scheme is determined based on the energy information obtained by the upper-level decision module and the preset upper-level cost target.

6. The method according to claim 5, characterized in that The step of modifying the current energy parameter based on the received first scheduling scheme and the preset allocation method to obtain the first energy parameter comprises: The first scheduling scheme and the current energy parameters are processed by a preset optimization model to obtain optimized energy parameters; Modifying the current energy parameters into optimized energy parameters; Based on the preset allocation method and the preset profit allocation target, the optimized energy parameter is modified to obtain the first energy parameter.

7. The method according to claim 6, characterized in that The first scheduling scheme and the current energy parameters are processed by a preset optimization model to obtain optimized energy parameters, including: Determine the power transaction cost and the total operation cost of the system based on the first dispatching scheme and the current energy parameters; Obtaining operation benefits based on the electricity transaction cost and the total operation cost of the system; Based on the operating benefits and preset operating benefit targets, the current energy parameters are modified through a preset optimization model to obtain optimized energy parameters.

8. A comprehensive energy dispatching platform, characterized in that: The comprehensive energy dispatching platform includes: The upper-layer decision module obtains energy information of each area, determines a first scheduling scheme based on the energy information and a preset upper-layer cost target, and sends the first scheduling scheme to the lower-layer execution module, so that the lower-layer execution module modifies the current energy parameter based on the first scheduling scheme and a preset allocation method to obtain a first energy parameter, and sends the first energy parameter to the local area, adjusts the first scheduling scheme based on the first energy parameter, and obtains a target scheduling scheme; The lower-level execution module obtains current energy parameters, modifies the current energy parameters based on the received first scheduling scheme and the preset allocation method to obtain first energy parameters, and sends the first energy parameters to the upper-level decision module so that the upper-level decision module can adjust the first scheduling scheme based on the first energy parameters to obtain a target scheduling scheme, wherein the first scheduling scheme is determined based on the energy information obtained by the upper-level decision module and the preset upper-level cost target.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the integrated energy scheduling method according to any one of claims 1 to 7 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the integrated energy scheduling method according to any one of claims 1 to 7 are implemented.

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