A virtual power plant optimization method considering comprehensive energy efficiency planning strategy

Abstract

The application discloses a kind of virtual power plant optimization methods considering comprehensive energy efficiency planning strategy, steps include: step S1: setting five comprehensive energy efficiency indexes: energy utilization efficiency, maintenance cost, peak shaving capacity, new energy consumption rate and power generation carbon emission;Step S2: determine the weight of five comprehensive energy efficiency indexes respectively;Step S3: establish virtual power plant optimization model considering comprehensive energy efficiency and operation output;Step S4: solve the above optimization model, the solution is used as the set value of virtual power plant operation, and makes virtual power plant run according to set value.The application considers the comprehensive energy efficiency of virtual power plant, proposes corresponding virtual power plant optimization model, completes virtual power plant optimization by selecting the distributed power supply meeting the requirements, so as to realize the optimal scheduling of virtual power plant, and improve the comprehensive energy efficiency.

Description

Technical Field

[0001] This invention belongs to the field of virtual power plant power generation, and specifically relates to an optimization method for a virtual power plant. Background Technology

[0002] The global shortage of fossil fuels is a problem that has drawn worldwide attention. At the same time, the greenhouse gases produced by burning fossil fuels are causing frequent severe weather and environmental problems related to the greenhouse effect. Coal-fired power is the largest source of global carbon emissions, accounting for 42%. To reduce carbon emissions, it is necessary to reduce the use of coal-fired power generation, and developing renewable energy generation can solve the problem of carbon emissions from the combustion of fossil fuels.

[0003] In recent years, distributed energy generation, primarily based on wind and solar renewable energy, has developed rapidly worldwide. While these renewable energy-based distributed power sources can reduce the reliance on thermal power units to some extent, distributed energy often suffers from drawbacks such as high volatility and small unit capacity, making it difficult for them to participate independently in the electricity market. The emergence of virtual power plants has effectively solved the volatility and capacity problems of distributed power sources. The term "virtual power plant" first appeared in the FENIX project and subsequently developed rapidly in Europe and the United States, establishing several demonstration projects that can be learned from and promoted, such as the FENIX project, the virtual power plant demonstration project developed by Germany's RWE power generation company in cooperation with Siemens, and Denmark's EDISION pilot project. China's virtual power plant engineering demonstration construction is in a rapid development stage. The Hebei North Virtual Power Plant Demonstration Project was put into operation in early 2019, followed by a key project: the Shanghai Huangpu District Pilot Commercial Building Virtual Power Plant Project. Virtual power plants combine distributed power sources, energy storage equipment, and controllable loads to participate in market operation in the form of virtual power plants, thereby solving the problems of uncontrollable output and random volatility when wind and solar distributed power generation participates in the market alone. By enabling distributed wind and solar power generation to participate in the electricity market through virtual power plants, it is helpful to promote the consumption of new energy sources and bring greater economic benefits.

[0004] However, existing virtual power plants do not take into account the issue of comprehensive energy efficiency during operation, resulting in poor actual comprehensive energy efficiency. Summary of the Invention

[0005] This invention proposes a virtual power plant optimization method that considers a comprehensive energy efficiency planning strategy. Its purpose is to optimize the scheduling of virtual power plants and improve their comprehensive energy efficiency.

[0006] The technical solution of this invention is as follows:

[0007] A virtual power plant optimization method considering a comprehensive energy efficiency planning strategy includes the following steps:

[0008] Step S1: Based on the evaluation strategy of the comprehensive energy efficiency of virtual power plants and the relevant factors affecting the comprehensive energy efficiency of virtual power plants, five comprehensive energy efficiency indicators are set: energy utilization efficiency, maintenance cost, peak shaving capacity, renewable energy consumption rate and carbon emissions from power generation.

[0009] Step S2: Determine the weights of the five comprehensive energy efficiency indicators;

[0010] Step S3: Establish a virtual power plant optimization model that considers comprehensive energy efficiency and operational output. The optimization model includes a comprehensive energy efficiency objective function and an operational output objective function for the virtual power plant. The comprehensive energy efficiency objective function is established based on the five comprehensive energy efficiency indicators. The decision variables of the optimization model are a set of 0-1 variables that correspond one-to-one with the distributed power generation equipment in the virtual power plant. A variable of 1 indicates that the corresponding distributed power generation equipment is put into operation, and a variable of 0 indicates that the corresponding distributed power generation equipment is not put into operation.

[0011] Step S4: Solve the above optimization model, use the solution as the set value for the operation of the virtual power plant, and make the virtual power plant operate according to the set value.

[0012] As a further improvement to the virtual power plant optimization method that considers the comprehensive energy efficiency planning strategy:

[0013] (1) The energy utilization efficiency is calculated as follows:

[0014] ;

[0015] in, The effective energy generated by the virtual power plant after selecting and putting distributed power generation equipment into operation according to the decision variable values; This represents the total energy consumption of the virtual power plant after selecting distributed power generation equipment based on the decision variable values;

[0016] (2) The maintenance cost is calculated as follows:

[0017] ;

[0018] In the formula, This represents the total maintenance cost of the virtual power plant, where N represents the total number of distributed power generation devices. Indicates the first Maintenance costs of distributed power devices The decision variable, in 0-1 form, represents whether to invest in the first... One distributed power supply device;

[0019] (3) The calculation method for peak-shaving capacity is as follows:

[0020] ;

[0021] This indicates the adjustable power output of the virtual power plant during peak operation. Indicates the first The adjustable power output of a distributed power supply device during peak operation;

[0022] (4) The calculation method for the new energy consumption rate is as follows:

[0023] ;

[0024] Indicates the selected number The set power of a distributed power device at time t within the optimization time. This represents the total energy used by the virtual power plant;

[0025] (5) The calculation method for carbon emissions from power generation is as follows:

[0026] ;

[0027] This represents the combined carbon emissions of a virtual power plant. For the first Average carbon emissions per unit energy of distributed power generation equipment Indicates the first The energy generated by a distributed power supply device.

[0028] As a further improvement to the virtual power plant optimization method considering the comprehensive energy efficiency planning strategy: before establishing the comprehensive energy efficiency objective function using the five comprehensive energy efficiency indicators, all indicators are first listed using an exhaustive method. The combination of the two results in There are N different decision variable groups, each containing N variables. Then, based on the decision variable set, we obtain The five comprehensive energy efficiency indicators mentioned in Group 1 are set as follows: The five comprehensive energy efficiency indicators in the decision variable group are as follows: , Then proceed as follows: Normalize:

[0029] ;

[0030] for The normalized value, for The first The minimum value of the comprehensive energy efficiency index, for The first The maximum value of the comprehensive energy efficiency index.

[0031] As a further improvement to the virtual power plant optimization method that considers the comprehensive energy efficiency planning strategy:

[0032] The overall energy efficiency objective function is:

[0033] ;

[0034] For the first The weight of each comprehensive energy efficiency indicator.

[0035] As a further improvement to the virtual power plant optimization method that considers the comprehensive energy efficiency planning strategy, in step S2, the weights are determined by a combined weighting method.

[0036] As a further improvement to the virtual power plant optimization method that considers the comprehensive energy efficiency planning strategy:

[0037] The objective function for the output of the operation is:

[0038] ;

[0039] For the first Distributed power devices within the optimization time Output power at any moment In the preset target running curve The target power of the virtual power plant at any given time.

[0040] As a further improvement to the virtual power plant optimization method considering the comprehensive energy efficiency planning strategy, the constraints of the optimization model include:

[0041] (1) Upper and lower limits of output power constraints for distributed power supply equipment:

[0042] ;

[0043] In the formula: and The first A distributed power device in The upper and lower limits of the output at any given time;

[0044] (2) Ramp-up rate constraints for distributed power supply equipment:

[0045] ;

[0046] and The first The upper and lower limits of the ramp rate for a distributed power supply device.

[0047] As a further improvement to the virtual power plant optimization method considering the comprehensive energy efficiency planning strategy, when solving the optimization model, first select the decision variable group that can make the function value of the output objective function within a preset range, then input the selected decision variable group into the comprehensive energy efficiency objective function, and take the decision variable group corresponding to the maximum value of the comprehensive energy efficiency objective function as the solution result of the optimization model.

[0048] Compared with existing technologies, this invention has the following beneficial effects: This invention considers the comprehensive energy efficiency of virtual power plants and proposes a corresponding virtual power plant optimization model. Specifically, this invention selects five indicators—efficiency, maintenance cost, peak-shaving capacity, renewable energy consumption rate, and power generation carbon emissions—as the comprehensive energy efficiency indicators of virtual power plants. These indicators are then quantified and normalized. By defining the comprehensive energy efficiency of virtual power plants and calculating related indicators, an evaluation value for the comprehensive energy efficiency of virtual power plants is obtained. Multi-objective optimization planning is then performed using the comprehensive energy efficiency evaluation value and the operating output curve as objectives. Distributed power sources that meet the requirements are selected to complete the optimization of the virtual power plant, thereby achieving optimized scheduling of the virtual power plant and improving comprehensive energy efficiency. Attached Figure Description

[0049] Figure 1 This is a flowchart of the virtual power plant optimization method of the present invention;

[0050] Figure 2 This is a flowchart of the solution process for the virtual power plant optimization model of the present invention;

[0051] Figure 3 This is a comparison chart of the output capacity of the virtual power plant optimization plan in the embodiment. Detailed Implementation

[0052] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are for illustrative purposes only and are not intended to limit the scope of the invention. The invention is described more specifically in the following paragraphs by way of example with reference to the accompanying drawings. The advantages and features of the invention will become clearer from the following description and claims. It should be noted that the drawings are in a very simplified form and use non-precise proportions, and are only used to facilitate and clarify the illustration of the embodiments of the invention.

[0053] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0054] like Figure 1 and 2 A virtual power plant optimization method considering a comprehensive energy efficiency planning strategy, comprising the following steps:

[0055] Step S1: Based on the evaluation strategy of the comprehensive energy efficiency of virtual power plants and the relevant factors affecting the comprehensive energy efficiency of virtual power plants, five comprehensive energy efficiency indicators are set: energy utilization efficiency, maintenance cost, peak shaving capacity, renewable energy consumption rate and carbon emissions from power generation.

[0056] Comprehensive energy efficiency refers to the system components and total energy consumption that play a role in the entire energy utilization process. For the comprehensive energy efficiency of a virtual power plant, the comprehensive energy efficiency of a typical thermal power plant can be referenced to propose comprehensive energy efficiency indicators compatible with virtual power plants.

[0057] (1) The energy utilization efficiency refers to the degree of effectiveness of the virtual power plant in utilizing energy, that is, the ratio of the effective energy actually generated by the virtual power plant to the total energy actually consumed. The calculation method is as follows:

[0058] ;

[0059] in, The effective energy generated by the virtual power plant after selecting and putting distributed power generation equipment into operation according to the decision variable values; This represents the total energy consumption of the virtual power plant after selecting and investing in distributed power generation equipment based on the decision variable values.

[0060] (2) The maintenance cost mainly refers to the aggregator cost, which includes the sum of the maintenance costs of the actually selected distributed power equipment. The calculation method is as follows:

[0061] ;

[0062] In the formula, This represents the total maintenance cost of the virtual power plant, where N represents the total number of distributed power generation devices. Indicates the first Maintenance costs of distributed power devices The decision variable is in 0-1 form, representing whether to invest in the first round. A distributed power supply device.

[0063] (3) Peak-shaving capacity refers to the power that a virtual power plant can adjust during peak operation, and is calculated as follows:

[0064] ;

[0065] This indicates the adjustable power output of the virtual power plant during peak operation. Indicates the first The adjustable power output of a distributed power supply device during peak operation.

[0066] (4) The new energy consumption rate refers to the utilization rate of new energy by the virtual power plant. This paper uses the percentage of new energy power generation to the total energy of the virtual power plant, and the calculation method is as follows:

[0067] ;

[0068] Indicates the selected number The set power of a distributed power device at time t within the optimization time. This represents the total energy used by the virtual power plant.

[0069] (5) The average carbon emissions per kilowatt-hour vary depending on the power generation technology. Under current carbon neutrality policies, power generation carbon emissions can also be used as one of the indicators of the overall energy efficiency of a virtual power plant. The calculation method for power generation carbon emissions is as follows:

[0070] ;

[0071] This represents the combined carbon emissions of a virtual power plant. For the first Average carbon emissions per unit energy of distributed power generation equipment Indicates the first The energy generated by a distributed power supply device.

[0072] Step S2: The combined weighting method effectively combines subjective weighting and objective weighting. Based on the specific analysis of the actual data of the five comprehensive energy efficiency indicators, the weights of the five comprehensive energy efficiency indicators are reasonably determined.

[0073] Step S3: Establish a virtual power plant optimization model that considers overall energy efficiency and operational output. In establishing this model, the overall energy efficiency is considered from the perspective of a general power plant, and the corresponding constraints are obtained by combining the conditions of a virtual power plant. An implicit enumeration method is used, that is, using variables that can only be 0 or 1, branching, and bounding to find the optimal solution.

[0074] The optimization model includes a comprehensive energy efficiency objective function and an operational output objective function for the virtual power plant. The comprehensive energy efficiency objective function is established based on the five comprehensive energy efficiency indicators. The decision variables of the optimization model are a set of 0-1 variables that correspond one-to-one with the distributed power generation equipment in the virtual power plant. A variable of 1 indicates that the corresponding distributed power generation equipment is put into operation, and a variable of 0 indicates that the corresponding distributed power generation equipment is not put into operation.

[0075] Specifically, before establishing the comprehensive energy efficiency objective function using the five comprehensive energy efficiency indicators, all indicators should be listed using an exhaustive method. The combination of the two results in There are N different decision variable groups, each containing N variables. Then, based on the decision variable set, we obtain The group describes five comprehensive energy efficiency indicators.

[0076] To eliminate the influence of the dimensional units in the five indicators, data normalization is required. The data normalization method used in this invention is to uniformly scale the five comprehensive energy efficiency indicators to the interval [0,1].

[0077] Let the first The five comprehensive energy efficiency indicators in the decision variable group are as follows: , Then proceed as follows: Normalize:

[0078] ;

[0079] for The normalized value, for The first The minimum value of the comprehensive energy efficiency index, for The first The maximum value of the comprehensive energy efficiency index.

[0080] The overall energy efficiency objective function is:

[0081] ;

[0082] For the first The weight of each comprehensive energy efficiency indicator.

[0083] The objective function for the output of the operation is:

[0084] ;

[0085] For the first Distributed power devices within the optimization time Output power at any moment In the preset target running curve The target power of the virtual power plant at any given time.

[0086] Furthermore, considering a typical power plant and its overall energy efficiency, and combining this with the conditions of a virtual power plant, the corresponding constraints are derived. The constraints of the optimization model include:

[0087] (1) Upper and lower limits of output power constraints for distributed power supply equipment:

[0088] ;

[0089] In the formula: and The first A distributed power device in The upper and lower limits of the output at any given time.

[0090] (2) Ramp-up rate constraints for distributed power supply equipment:

[0091] ;

[0092] and The first The upper and lower limits of the ramp rate for a distributed power supply device.

[0093] Step S4: Solve the above optimization model, use the solution as the set value for the operation of the virtual power plant, and make the virtual power plant operate according to the set value.

[0094] When solving the optimization model, first select the decision variable set that can make the function value of the output objective function within a preset range. Then, input the selected decision variable set into the comprehensive energy efficiency objective function, and take the decision variable set corresponding to the maximum value of the comprehensive energy efficiency objective function as the solution result of the optimization model.

[0095] like Figure 3 For the results of randomly aggregated virtual power plants, a comparative analysis of the operational output clearly shows that the results obtained by the optimization algorithm should be closer to the target value and better meet the requirements. The comprehensive energy efficiency analysis results of the randomly aggregated virtual power plants are shown in Table 1:

[0096] Table 1. Overall Energy Efficiency Results of Randomly Aggregated Virtual Power Plants

[0097] Overall Energy Efficiency Index Assessment value Energy utilization efficiency 0.29 Maintenance costs 0.69 Peak shaving capability 0.26 New energy consumption rate 0.58 Carbon emissions from power generation 0.80 Comprehensive Energy Efficiency Evaluation Value 2.62

[0098] Table 2. Optimized Overall Energy Efficiency Results of the Virtual Power Plant

[0099] Overall Energy Efficiency Index Assessment value Energy utilization efficiency 0.13 Maintenance costs 0.65 Peak shaving capability 0.31 New energy consumption rate 0.75 Carbon emissions from power generation 0.81 Comprehensive Energy Efficiency Evaluation Value 2.66

[0100] The results in Tables 1 and 2 allow for a comparison of the optimized aggregated virtual power plant. The overall energy efficiency evaluation values ​​differ. Based on the weights, the overall energy efficiency evaluation value of the virtual power plant is 2.62, which is lower than the 2.66 of the optimized aggregated virtual power plant. Therefore, it can be concluded that the overall energy efficiency of the random aggregated virtual power plant construction scheme is lower than that of the optimized aggregated virtual power plant.

[0101] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Those skilled in the art can readily implement the present invention based on the accompanying drawings and the above description. However, any modifications, alterations, or variations made by those skilled in the art without departing from the scope of the present invention, utilizing the disclosed technical content, are equivalent embodiments of the present invention. Furthermore, any modifications, alterations, or variations made to the above embodiments based on the essential technology of the present invention are still within the protection scope of the present invention.

Claims

1. A virtual power plant optimization method considering a comprehensive energy efficiency planning strategy, characterized by the following steps: include: Step S1: Based on the evaluation strategy of the comprehensive energy efficiency of virtual power plants and the relevant factors affecting the comprehensive energy efficiency of virtual power plants, five comprehensive energy efficiency indicators are set: energy utilization efficiency, maintenance cost, peak shaving capacity, renewable energy consumption rate and carbon emissions from power generation. (1) The energy utilization efficiency is calculated as follows: ； in, The effective energy generated by the virtual power plant after selecting and putting distributed power generation equipment into operation according to the decision variable values; This represents the total energy consumption of the virtual power plant after selecting distributed power generation equipment based on the decision variable values; (2) The maintenance cost is calculated as follows: ； In the formula, This represents the total maintenance cost of the virtual power plant, where N represents the total number of distributed power generation devices. Indicates the first Maintenance costs of distributed power devices The decision variable, in 0-1 form, represents whether to invest in the first... One distributed power supply device; (3) The calculation method for peak-shaving capacity is as follows: ； This indicates the adjustable power output of the virtual power plant during peak operation. Indicates the first The adjustable power output of a distributed power supply device during peak operation; (4) The calculation method for the new energy consumption rate is as follows: ； Indicates the selected number The set power of a distributed power device at time t within the optimization time. This represents the total energy used by the virtual power plant; (5) The calculation method for carbon emissions from power generation is as follows: ； This represents the combined carbon emissions of a virtual power plant. For the first Average carbon emissions per unit energy of distributed power generation equipment Indicates the first The energy generated by a distributed power supply device; Step S2: Determine the weights of the five comprehensive energy efficiency indicators; Before using the five comprehensive energy efficiency indicators to establish the comprehensive energy efficiency objective function, all indicators should be listed exhaustively. The combination of the two results in There are N different decision variable groups, each containing N variables. Then, based on the decision variable set, we obtain The five comprehensive energy efficiency indicators mentioned in Group 1 are set as follows: The five comprehensive energy efficiency indicators in the decision variable group are as follows: , Then proceed as follows: Normalize: ； for The normalized value, for The first The minimum value of the comprehensive energy efficiency index, for The first The maximum value of the comprehensive energy efficiency index; Step S3: Establish a virtual power plant optimization model that considers comprehensive energy efficiency and operational output. The optimization model includes a comprehensive energy efficiency objective function and an operational output objective function for the virtual power plant. The comprehensive energy efficiency objective function is established based on the five comprehensive energy efficiency indicators. The decision variables of the optimization model are a set of 0-1 variables that correspond one-to-one with the distributed power generation equipment in the virtual power plant. A variable of 1 indicates that the corresponding distributed power generation equipment is put into operation, and a variable of 0 indicates that the corresponding distributed power generation equipment is not put into operation. The overall energy efficiency objective function is: ； For the first The weight of each comprehensive energy efficiency indicator; The objective function for the output of the operation is: ； For the first Distributed power devices within the optimization time Output power at any moment In the preset target running curve The target power of the virtual power plant at any given time; Step S4: Solve the above optimization model, use the solution as the set value for the operation of the virtual power plant, and make the virtual power plant operate according to the set value.

2. The virtual power plant optimization method considering comprehensive energy efficiency planning strategy as described in claim 1, characterized in that... In step S2, the weights are determined using a combined weighting method.

3. The virtual power plant optimization method considering comprehensive energy efficiency planning strategy as described in claim 1, characterized in that: The constraints of the optimization model include: (1) Upper and lower limits of output power constraints for distributed power supply equipment: ； In the formula: and The first A distributed power device in The upper and lower limits of the output at any given time; (2) Ramp-up rate constraints for distributed power supply equipment: ； and The first The upper and lower limits of the ramp rate for a distributed power supply device.

4. The virtual power plant optimization method considering comprehensive energy efficiency planning strategy as described in claim 3, characterized in that: When solving the optimization model, first select the decision variable set that can make the function value of the output objective function within a preset range. Then, input the selected decision variable set into the comprehensive energy efficiency objective function, and take the decision variable set corresponding to the maximum value of the comprehensive energy efficiency objective function as the solution result of the optimization model.

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