Virtual power plant comprehensive investment benefit evaluation method

Abstract

This invention discloses a comprehensive investment benefit evaluation method for virtual power plants. The method constructs a three-level indicator system comprising four criterion layers: direct benefits, indirect benefits, cost input, and adaptability. Indicators are differentiated according to four major application scenarios and weights are set based on the AHP method. A dynamic weighting mechanism is established, with periodic calibration every two years and adjustments triggered by policy / market / technology changes. National policy updates are completed within three months of the indicator / weight adjustment. The criterion layer results are calculated using a unified quantitative formula, and the annual comprehensive net benefit is obtained through weighted fusion. The full life-cycle benefit is then calculated based on the NPV model. Evaluation data comes from three authoritative channels: technical parameters, project measurements, and policy standards, and has been verified. The method also clarifies the specific indicator groups under each criterion layer and the specific formulas for quantification, benefit calculation, and full life-cycle benefit calculation, making it suitable for investment decisions and grid planning of virtual power plants in multiple regions and with multiple resource types.

Description

Technical Field

[0001] This invention relates to the field of virtual power plant technology and energy economic assessment, and in particular to a comprehensive investment benefit assessment method for the entire life cycle of a virtual power plant that is adaptable to multiple scenarios and has dynamically adjustable weights. This method is applicable to scenarios such as virtual power plant project investment decision-making and power grid planning optimization. Background Technology

[0002] With the advancement of the "dual carbon" goals, the new power system exhibits core characteristics of "new energy as the main body, power electronics as support, and source-grid-load-storage interaction as the mode." The volatility and intermittency issues brought about by the high proportion of new energy integration are becoming increasingly prominent. As a distributed resource aggregation carrier, virtual power plants play a key role in ensuring power supply, new energy consumption, and improving the market system. Their application scenarios have extended to four core areas: system operation and regulation, demand-side management, market trading, and new energy consumption.

[0003] Current virtual power plant investment benefit assessment models have the following key flaws:

[0004] 1. Single value dimension: It only focuses on direct economic benefits such as arbitrage in the power market and demand response subsidies, while ignoring indirect values ​​such as alleviating distribution network congestion, improving the consumption of new energy sources, and reducing carbon emissions. The evaluation results cannot fully reflect the comprehensive benefits of virtual power plants.

[0005] 2. Insufficient scenario adaptability: No specific evaluation indicators and quantitative logic were designed for the functional positioning differences of the four major application scenarios. The use of a uniform standard to evaluate projects in different scenarios resulted in low accuracy.

[0006] 3. Lack of dynamic weighting mechanism: The weights of the indicators are static and fixed, and are not dynamically adjusted in combination with factors such as regional policies, market mechanism maturity, and technological progress, making it difficult to adapt to the assessment needs of different regions and different stages of development.

[0007] 4. Lack of full lifecycle accounting: Some models rely on short-term static data, do not include the full lifecycle costs such as initial investment, operation and maintenance, and decommissioning, and do not consider the time value of money, thus failing to support long-term investment decisions.

[0008] To address the aforementioned issues, there is an urgent need to develop a quantitative evaluation method for the comprehensive investment benefits of virtual power plants, characterized by comprehensive indicators, scenario differentiation, dynamic weighting, and full-cycle accounting, to fill the existing technological gap. Summary of the Invention

[0009] The technical problem to be solved by this invention is to provide a quantitative evaluation method for the comprehensive investment benefits of virtual power plants in multiple scenarios. By constructing a three-level indicator system, designing scenario-based differentiated weights, establishing a dynamic weight adjustment mechanism and a full life cycle quantitative model, the method achieves comprehensiveness, accuracy and practicality in the evaluation of investment benefits of virtual power plants, and provides a reliable quantitative basis for investment decisions and power grid planning for virtual power plant projects.

[0010] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: a smart assessment method for the cleanliness of photovoltaic panels based on multi-feature fusion, comprising the following steps:

[0011] 1) Construct a three-level indicator system: target layer, criterion layer, and indicator layer. The target layer is the comprehensive investment benefit index of virtual power plants. The criterion layer includes four core dimensions: direct benefits, indirect benefits, cost input, and adaptability. The indicator layer is configured with corresponding quantitative indicators according to four application scenarios: system operation and regulation, demand-side management, market transactions, and new energy consumption.

[0012] 2) Construct a judgment matrix based on the Analytic Hierarchy Process (AHP) and perform a consistency test to set differentiated weights and scores for the criterion layer and the indicator layer under the four major application scenarios;

[0013] 3) Establish a regular and trigger-based dynamic weight adjustment mechanism to calibrate the weights. The mechanism includes regular calibration every two years, as well as trigger-based adjustments when regional policies, electricity market mechanisms, and virtual power plant-related technologies change.

[0014] 4) The annual quantitative results of each criterion level are calculated using a unified quantitative formula. Direct benefits, indirect benefits, adaptability and cost input are weighted and integrated to obtain the project's annual comprehensive net benefit.

[0015] 5) Based on the net present value (NPV) model, incorporate the time value of money to calculate the comprehensive investment benefits of the virtual power plant throughout its entire life cycle.

[0016] Preferably, the direct revenue criterion layer includes a market transaction revenue indicator group and a demand response revenue indicator group;

[0017] The market transaction revenue indicator group includes spot market arbitrage revenue, ancillary service market revenue, capacity market revenue, and green electricity transaction revenue;

[0018] The demand response revenue indicator group includes demand response subsidy revenue, user electricity cost savings sharing, and value-added service revenue.

[0019] Preferably, the indirect benefit criterion layer includes a system optimization value index group and an environmental and ecological value index group;

[0020] The system optimization value index group includes the value of distribution network investment substitution, the value of power outage loss reduction, the value of transmission loss reduction, and the value of new energy consumption enhancement.

[0021] The environmental and ecological value index group includes carbon emission reduction benefits, fossil energy substitution benefits, and pollutant emission reduction value.

[0022] Preferably, the cost input criteria layer includes a construction and operation cost indicator group and a full-cycle additional cost indicator group;

[0023] The construction and operation cost indicator group includes initial investment cost, unit construction cost, annual operation and maintenance cost coefficient, and financing cost;

[0024] The full-cycle additional cost indicator group includes decommissioning and disposal costs, technology upgrade costs, and compliance management costs.

[0025] Preferably, the adaptation capability criterion layer includes a scenario adaptation indicator group and a policy-market adaptation indicator group;

[0026] The scenario adaptation index group includes resource aggregation capability, response speed adaptation, load matching degree, and cross-scenario migration capability.

[0027] The policy market adaptability indicator group includes multi-market participation capability, policy subsidy acquisition amount, electricity price fluctuation adaptability, and regional policy adaptability.

[0028] Preferably, in the dynamic weight adjustment mechanism, when the state introduces new policies related to virtual power plants, the indicators of the indicator layer or the weight adjustment of the criteria layer and indicator layer must be completed within 3 months; the periodic calibration is to recalibrate the weights by combining expert scoring with the electricity market price level and the performance upgrade of energy storage equipment.

[0029] Preferably, the unified quantization formula in step 4 is:

[0030]

[0031] in, The quantification result of the i-th criterion layer (i=1 corresponds to direct benefits, i=2 corresponds to indirect benefits, i=3 corresponds to cost input, i=4 corresponds to adaptability). This represents the actual measured value of the j-th indicator under the i-th criterion layer; This represents the weight percentage of the j-th indicator under the i-th criterion layer; Let be the number of indicators under the i-th criterion layer.

[0032] Preferably, the formula for calculating the annual comprehensive net income in step 4 is as follows:

[0033]

[0034] in: The weighted average value after consolidation in year t reflects the overall net income level for that year. , , , Let be the quantitative results of the direct benefits, indirect benefits, cost inputs, and adaptability criteria layers for year t, respectively. This is the adaptation capability correction factor for that year, ranging from 0 to 1.

[0035] Preferably, the calculation formula for the Net Present Value (NPV) model in step 5 is as follows:

[0036]

[0037] Wherein: NPV represents the comprehensive investment benefit throughout the entire life cycle of a virtual power plant. t is the benchmark discount rate; n is the total lifespan; t is the accounting year.

[0038] Preferably, the data used in the assessment are all from three types of traceable and authoritative channels: technical parameter data, project measured data, and policy standard data. All data are verified and outlier removal before being used for quantitative calculation of the comprehensive net income at each standard level and annually.

[0039] The beneficial effects of adopting the above technical solution are as follows:

[0040] Comprehensive indicator coverage: Breaking through the limitations of traditional models that only focus on direct benefits, it incorporates indirect values ​​such as distribution network investment substitution, carbon emission reduction, and reduction of power outage losses into the evaluation. At the same time, it covers four dimensions: revenue, cost, external contribution, and adaptability, fully reflecting the comprehensive investment value of virtual power plants.

[0041] High scenario adaptability: For the functional positioning differences of the four core application scenarios, exclusive indicators and differentiated weights are configured, and the evaluation error is reduced by more than 30% compared with the traditional unified model, accurately matching the diverse functional positioning of the virtual power plant.

[0042] Dynamically adjustable weights: Establish a dual weight adjustment mechanism of regular and trigger-based adjustments to adapt to changes in regional policies, market mechanisms, and technological progress, and meet the assessment needs of different regions and different stages of development;

[0043] Calculating the entire life cycle: Based on the net present value model, the costs of the entire life cycle, including initial investment, operation and maintenance, and decommissioning, are included, and the time value of money is considered. The assessment results can effectively support long-term investment decisions for virtual power plants.

[0044] Highly practical: Indirect value adopts a standardized quantitative formula, and the data source is public policies, industry standards or project measurements. There is no need to build an additional complex monitoring system, and it can be directly embedded into the virtual power plant operation platform.

[0045] Wide applicability: The indicator system can be adapted to different aggregated resources such as energy storage, V2G, and flexible loads. Through weight correction coefficients, it can be adapted to different regional policies and market mechanisms to meet the application needs of multiple regions and multiple resource types. Attached Figure Description

[0046] Figure 1 This is a schematic diagram of the hierarchical structure of the present invention;

[0047] Figure 2 This is a schematic diagram of the calculation process for the comprehensive benefit model of this invention;

[0048] Figure 3 This is a weight difference allocation table diagram according to an embodiment of the present invention. Detailed Implementation

[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] like Figure 1 As shown, this invention discloses an intelligent assessment method for the cleanliness of photovoltaic panels based on multi-feature fusion, comprising the following steps:

[0051] 1) Construct a three-level indicator system: target layer, criterion layer, and indicator layer. The target layer is the comprehensive investment benefit index of virtual power plants. The criterion layer includes four core dimensions: direct benefits, indirect benefits, cost input, and adaptability. The indicator layer is configured with corresponding quantitative indicators according to four application scenarios: system operation and regulation, demand-side management, market transactions, and new energy consumption.

[0052] 2) Construct a judgment matrix based on the Analytic Hierarchy Process (AHP) and perform a consistency test to set differentiated weights and scores for the criterion layer and the indicator layer under the four major application scenarios;

[0053] 3) Establish a regular and trigger-based dynamic weight adjustment mechanism to calibrate the weights. The mechanism includes regular calibration every two years, as well as trigger-based adjustments when regional policies, electricity market mechanisms, and virtual power plant-related technologies change.

[0054] 4) The annual quantitative results of each criterion level are calculated using a unified quantitative formula. Direct benefits, indirect benefits, adaptability and cost input are weighted and integrated to obtain the project's annual comprehensive net benefit.

[0055] 5) Based on the net present value (NPV) model, incorporate the time value of money to calculate the comprehensive investment benefits of the virtual power plant throughout its entire life cycle.

[0056] Specifically, the direct benefit criterion layer includes a market transaction benefit indicator group and a demand response benefit indicator group;

[0057] The market transaction revenue indicator group includes spot market arbitrage revenue, ancillary service market revenue, capacity market revenue, and green electricity transaction revenue;

[0058] The demand response revenue indicator group includes demand response subsidy revenue, user electricity cost savings sharing, and value-added service revenue.

[0059] Specifically, the indirect benefit criterion layer includes a system optimization value index group and an environmental and ecological value index group;

[0060] The system optimization value index group includes the value of distribution network investment substitution, the value of power outage loss reduction, the value of transmission loss reduction, and the value of new energy consumption enhancement.

[0061] The environmental and ecological value index group includes carbon emission reduction benefits, fossil energy substitution benefits, and pollutant emission reduction value.

[0062] Specifically, the cost input criteria layer includes a construction and operation cost indicator group and a full-cycle additional cost indicator group;

[0063] The construction and operation cost indicator group includes initial investment cost, unit construction cost, annual operation and maintenance cost coefficient, and financing cost;

[0064] The full-cycle additional cost indicator group includes decommissioning and disposal costs, technology upgrade costs, and compliance management costs.

[0065] Specifically, the adaptation capability criteria layer includes a scenario adaptation index group and a policy-market adaptation index group;

[0066] The scenario adaptation index group includes resource aggregation capability, response speed adaptation, load matching degree, and cross-scenario migration capability.

[0067] The policy market adaptability indicator group includes multi-market participation capability, policy subsidy acquisition amount, electricity price fluctuation adaptability, and regional policy adaptability.

[0068] Specifically, in the dynamic weight adjustment mechanism, when the state introduces new policies related to virtual power plants, the indicators of the indicator layer or the weights of the criteria layer and indicator layer must be supplemented within 3 months; the periodic calibration is to recalibrate the weights by combining expert scoring with the electricity market price level and the performance upgrade of energy storage equipment.

[0069] Specifically, the unified quantification formula mentioned in step 4 is:

[0070]

[0071] in, The quantification result of the i-th criterion layer (i=1 corresponds to direct benefits, i=2 corresponds to indirect benefits, i=3 corresponds to cost input, i=4 corresponds to adaptability). This represents the actual measured value of the j-th indicator under the i-th criterion layer; This represents the weight percentage of the j-th indicator under the i-th criterion layer; Let be the number of indicators under the i-th criterion layer.

[0072] Specifically, the formula for calculating the annual comprehensive net income mentioned in step 4 is as follows:

[0073]

[0074] in: The weighted average value after consolidation in year t reflects the overall net income level for that year. , , , Let be the quantitative results of the direct benefits, indirect benefits, cost inputs, and adaptability criteria layers for year t, respectively. This is the adaptation capability correction factor for that year, ranging from 0 to 1.

[0075] Preferably, the calculation formula for the Net Present Value (NPV) model in step 5 is as follows:

[0076]

[0077] Wherein: NPV represents the comprehensive investment benefit throughout the entire life cycle of a virtual power plant. t is the benchmark discount rate; n is the total lifespan; t is the accounting year.

[0078] Preferably, the data used in the assessment are all from three types of traceable and authoritative channels: technical parameter data, project measured data, and policy standard data. All data are verified and outlier removal before being used for quantitative calculation of the comprehensive net income at each standard level and annually.

[0079] like Figure 2 As shown, the specific implementation steps of the novel multi-scenario comprehensive investment benefit quantitative evaluation method for energy storage of the present invention are as follows:

[0080] Step 1: Determine the evaluation scenario and initial weights

[0081] Define the application scenario of the virtual power plant project to be evaluated (one or a combination of system operation regulation, demand-side management, market trading, and renewable energy consumption), and retrieve the initial weight data of the criteria layer and indicator layer for that scenario from the internal database. The weight values ​​are determined as follows: Figure 3 Execution of the differentiated weight table.

[0082] Step 2: Data Collection and Verification

[0083] Three categories of authoritative and traceable data are collected, covering all basic parameters corresponding to the three levels of indicators:

[0084] 1) Technical parameter data: Technical specifications of virtual power plant aggregation platform, national standard for energy storage equipment (GB / T36547-2018), industry statistical reports, etc., including charging and discharging efficiency, load response speed, adjustable capacity, etc.;

[0085] 2) Actual measured data of the project: virtual power plant operation ledger, power grid dispatch center records, electricity consumption files of industrial and commercial users, etc., including annual response times, actual output of peak shaving / frequency regulation, and user electricity cost savings, etc.

[0086] 3) Policy and standard data: "Guiding Opinions on Accelerating the Development of Virtual Power Plants", announcements from provincial power trading centers, rules for ancillary services markets, including peak shaving / frequency regulation compensation standards, carbon prices, and loss of electricity costs (VOLL).

[0087] After data collection, the data is cleaned according to data verification criteria to remove outliers and missing values, ensuring data quality.

[0088] Step 3: Weight Determination and Adjustment

[0089] 1) Calculate the weights of the indicator layer using the Analytic Hierarchy Process (AHP), construct a judgment matrix, and perform a consistency check;

[0090] 2) Make partial adjustments to the basic weights based on regional policy characteristics (such as the activity level of the carbon market, the maturity of the provincial spot market, and the characteristics of high curtailment areas);

[0091] 3) After correction, perform the CR<0.1 consistency test again. If the test passes, determine the final weight; otherwise, readjust.

[0092] Step 4: Quantitative Calculation of Overall Returns

[0093] 1) Substitute the verified actual measured values ​​and final weights into the quantitative formula of the criteria layer to calculate the annual quantitative results of direct benefits, indirect benefits, cost input, and adaptability respectively;

[0094] 2) Substitute the annual quantitative results of the standard layer into the annual comprehensive net income formula to calculate the comprehensive net income for year t, where the adaptability correction coefficient is included. The value is determined based on the project's actual resource aggregation capacity and policy adaptability, and ranges from 0 to 1.

[0095] 3) Determine the benchmark discount rate Given the virtual power plant's entire lifespan (n years), the net income for each year is substituted into the net present value (NPV) formula to calculate the overall lifespan investment benefit (NPV). If NPV > 0, the project has investment value; the higher the NPV, the better the investment benefit.

[0096] Step 5: Dynamic Optimization and Update

[0097] Regular calibration: Every two years, the weights of the criteria layer and the indicator layer are recalibrated using an expert scoring method, taking into account the electricity market price level, advancements in virtual power plant aggregation technology, and performance upgrades of energy storage equipment.

[0098] Triggered adjustment: When the regional power structure changes, load characteristics are adjusted, or regional virtual power plant policies are updated, a local adjustment of weights will be initiated; when the state introduces new virtual power plant-related policies, the supplementation of indicators or adjustment of weights will be completed within 3 months to ensure that the evaluation system is consistent with national policies and market mechanisms.

Claims

1. A method for intelligent assessment of photovoltaic panel cleanliness based on multi-feature fusion, characterized in that, Includes the following steps: 1) Construct a three-level indicator system: target layer, criterion layer, and indicator layer. The target layer is the comprehensive investment benefit index of virtual power plants. The criterion layer includes four core dimensions: direct benefits, indirect benefits, cost input, and adaptability. The indicator layer is configured with corresponding quantitative indicators according to four application scenarios: system operation and regulation, demand-side management, market transactions, and new energy consumption. 2) Construct a judgment matrix based on the Analytic Hierarchy Process (AHP) and perform a consistency test to set differentiated weights and scores for the criterion layer and the indicator layer under the four major application scenarios; 3) Establish a regular and trigger-based dynamic weight adjustment mechanism to calibrate the weights. The mechanism includes regular calibration every two years, as well as trigger-based adjustments when regional policies, electricity market mechanisms, and virtual power plant-related technologies change. 4) The annual quantitative results of each criterion level are calculated using a unified quantitative formula. Direct benefits, indirect benefits, adaptability and cost input are weighted and integrated to obtain the project's annual comprehensive net benefit. 5) Based on the net present value (NPV) model, incorporate the time value of money to calculate the comprehensive investment benefits of the virtual power plant throughout its entire life cycle.

2. The intelligent assessment method for the cleanliness of photovoltaic panels based on multi-feature fusion according to claim 1, characterized in that, The direct revenue criterion layer includes a market transaction revenue indicator group and a demand response revenue indicator group. The market transaction revenue indicator group includes spot market arbitrage revenue, ancillary service market revenue, capacity market revenue, and green electricity transaction revenue; The demand response revenue indicator group includes demand response subsidy revenue, user electricity cost savings sharing, and value-added service revenue.

3. The intelligent assessment method for photovoltaic panel cleanliness based on multi-feature fusion according to claim 1, characterized in that, The indirect benefit criterion layer includes a system optimization value indicator group and an environmental and ecological value indicator group. The system optimization value index group includes the value of distribution network investment substitution, the value of power outage loss reduction, the value of transmission loss reduction, and the value of new energy consumption enhancement. The environmental and ecological value index group includes carbon emission reduction benefits, fossil energy substitution benefits, and pollutant emission reduction value.

4. The intelligent assessment method for the cleanliness of photovoltaic panels based on multi-feature fusion according to claim 1, characterized in that, The cost input criteria layer includes a construction and operation cost indicator group and a full-cycle additional cost indicator group. The construction and operation cost indicator group includes initial investment cost, unit construction cost, annual operation and maintenance cost coefficient, and financing cost; The full-cycle additional cost indicator group includes decommissioning and disposal costs, technology upgrade costs, and compliance management costs.

5. The intelligent assessment method for the cleanliness of photovoltaic panels based on multi-feature fusion according to claim 1, characterized in that, The adaptation capability criteria layer includes a scenario adaptation index group and a policy-market adaptation index group. The scenario adaptation index group includes resource aggregation capability, response speed adaptation, load matching degree, and cross-scenario migration capability. The policy market adaptability indicator group includes multi-market participation capability, policy subsidy acquisition amount, electricity price fluctuation adaptability, and regional policy adaptability.

6. The intelligent assessment method for the cleanliness of photovoltaic panels based on multi-feature fusion according to claim 1, characterized in that, In the aforementioned dynamic weight adjustment mechanism, when the state introduces new policies related to virtual power plants, the indicators of the indicator layer or the weight adjustments of the criteria layer and indicator layer must be completed within 3 months; the periodic calibration is to recalibrate the weights by combining expert scoring with the electricity market price level and the performance upgrade of energy storage equipment.

7. The intelligent assessment method for the cleanliness of photovoltaic panels based on multi-feature fusion according to claim 1, characterized in that, The unified quantification formula mentioned in step 4 is: ； in, The quantification result of the i-th criterion layer (i=1 corresponds to direct benefits, i=2 corresponds to indirect benefits, i=3 corresponds to cost input, i=4 corresponds to adaptability). This represents the actual measured value of the j-th indicator under the i-th criterion layer; This represents the weight percentage of the j-th indicator under the i-th criterion layer; Let be the number of indicators under the i-th criterion layer.

8. The intelligent assessment method for the cleanliness of photovoltaic panels based on multi-feature fusion according to claim 1, characterized in that, The formula for calculating the annual comprehensive net income mentioned in step 4 is as follows: ； in: The weighted average value after consolidation in year t reflects the overall net income level for that year. , , , Let be the quantitative results of the direct benefits, indirect benefits, cost inputs, and adaptability criteria layers for year t, respectively. This is the adaptation capability correction factor for that year, ranging from 0 to 1.

9. The intelligent assessment method for the cleanliness of photovoltaic panels based on multi-feature fusion according to claim 1, characterized in that, The formula for calculating the net present value (NPV) model described in step 5 is as follows: ； Wherein: NPV represents the comprehensive investment benefit throughout the entire life cycle of a virtual power plant. t is the benchmark discount rate; n is the total lifespan; t is the accounting year.

10. A method for evaluating the comprehensive investment benefits of a virtual power plant according to any one of claims 1 to 9, characterized in that, The data used in the assessment are all from three traceable and authoritative sources: technical parameter data, project measured data, and policy standard data. All data are verified and outlier removed before being used for quantitative calculation of the comprehensive net income at each standard level and annually.

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