CN113592134A - Power carbon emission assessment system and method based on energy data - Google Patents
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Abstract
The invention provides an electric power carbon emission evaluation system and system based on energy data. The assessment method specifically comprises the steps of firstly determining an area and a time period which need to be assessed for power carbon emission according to an assessment request for power carbon emission, acquiring corresponding energy data, optimizing the energy data through a weighted least square method, classifying the optimized energy data to obtain electric quantity components, and finally calculating a carbon emission assessment index through the electric quantity components and the energy data to assess the carbon emission. The method analyzes the acquired energy data by using a weighted least square method, fully utilizes the optimized energy data amount to calculate the power carbon emission index of a specific area in a specific time period, and realizes more comprehensive and accurate power carbon emission condition evaluation.
Description
Technical Field
The invention relates to the technical field of carbon emission assessment, in particular to a power carbon emission assessment system and method based on energy data.
Background
At present, the electric power industry still is an industry with a large carbon emission ratio, and occupies about four times of the carbon emission in the national whole social energy industry. And the electric power industry is in the important time period of energy low-carbon transformation at present, and effective evaluation on carbon emission can provide data basis for subsequent low-carbon transformation work. And by comparing the carbon emission condition evaluation results of the specific area at the specific time, the development effect of the low-carbon work can be judged, so that data support is provided for the optimization and improvement of the low-carbon work.
The carbon emission of the power industry is mainly concentrated on the electric energy supply side and generated by power generation of a fossil fuel unit, and when the carbon emission condition of the power generation side is quantified and counted, because the existing carbon emission condition evaluation method needs to use data from a plurality of systems, such as electric power quantity, coal-fired gas and exhaust gas detection, and the like, and the data have redundancy, the reasonable selection, cleaning and estimation of the data before the data are applied are also a great difficulty. And the overall power demand of the society is in a constantly fluctuating state, the carbon emission condition at the power generation side generally fluctuates seasonally, and if the carbon emission condition is difficult to effectively and reasonably evaluate only by using the detection data of the electric power quantity, the coal gas and the exhaust gas, the accurate evaluation result is difficult to obtain.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides a power carbon emission evaluation system and method based on energy data.
The purpose of the invention is realized by the following technical scheme:
an electric power carbon emission assessment method based on energy data comprises the following steps:
the method comprises the steps that firstly, an electric power carbon emission evaluation system receives an evaluation request of electric power carbon emission and determines an area and a time period which need to be evaluated according to the evaluation request, a data acquisition module acquires energy data of the area and the time period which are determined to be evaluated for the electric power carbon emission, and the data acquisition module transmits the acquired energy data to a data processing module;
the data processing module carries out state estimation processing on the energy data through a weighted least square method, so that energy data optimization is carried out;
classifying the optimized energy data by a data analysis module, and acquiring all electric quantity components contained in the electric quantity consumed in the area and the time period required to be subjected to electric power carbon emission evaluation according to the classification result;
and step four, the data analysis module calculates carbon emission evaluation indexes in areas and time periods needing power carbon emission evaluation according to the electric quantity components and the optimized energy data, and the data analysis module obtains power carbon emission evaluation results according to calculation results of the carbon emission evaluation indexes.
The energy data replaces traditional electric power electric quantity data, coal-fired gas data and exhaust gas detection data, the electric data and the energy carbon emission data are included in the energy data, the energy data can be directly acquired by collecting data such as electric quantity, load and components accessed by each level of power dispatching control center, and the situation that data types and data sources are various and effective evaluation cannot be carried out cannot occur. And the areas and time periods for carbon emission condition evaluation can be limited, and the effect of the low-carbon emission reduction work can be obtained by comparing the carbon emission evaluation results of the same area in different time periods, so that data support is provided for the arrangement optimization of the low-carbon emission reduction work. The energy data can be optimized through the weighted least square method, and the accuracy of an evaluation result can be further improved by evaluating according to the optimized energy data. The electric quantity components of the consumed electric quantity in the region are obtained through the classification of the optimized energy data, and the overall carbon emission condition in the region can be obtained through comprehensive analysis of each electric quantity component.
Further, the specific steps of the data processing module performing state estimation processing on the energy data by a weighted least square method in the second step are as follows:
2.1, establishing a state estimation function z of energy data;
2.2, determining a measurement weight matrix R of each state vector x in the state estimation function;
2.3, calculating a first-order Jacobian matrix H of the state estimation function;
2.4, calculating according to the measurement weight matrix R and the first-order Jacobian matrix H to obtain a gain matrix G;
2.5, calculating the iteration quantity delta x of each state vector x, judging whether the iteration quantity delta x is smaller than a rated precision threshold value, and if so, acquiring optimized energy data, namely x + delta x; otherwise, returning to the step 2.1 to recalculate until the iteration quantity Δ x of each state vector x is smaller than the rated precision threshold.
The weighted least square method can optimize the energy data, and the weight matrix is added into the energy data through the weighted least square method, so that the weight of each data component in the energy data is adjusted, and the accuracy of the power carbon emission evaluation is further improved.
Further, the specific formula of the state estimation function z is as follows:
z＝h(x)+e；
wherein: z is a state estimation function;
is a state vector, U is a voltage amplitude, I is a current amplitude,
is a phase angle, and w is a unit electric quantity carbon emission value of the unit; e is the measurement error; h (x) is a measurement function of the state vector;
the specific formula of the first-order Jacobian matrix H is as follows:
wherein: h is a first-order Jacobian matrix, U is a voltage amplitude, I is a current amplitude,
is a phase angle, and w is a unit electric quantity carbon emission value of the unit; h (U) is a measure of the voltage amplitude U, h (I) is a measure of the current amplitude I,
is a phase angle
H (w) is a measurement function of the unit electric quantity carbon emission value w of the unit;
the specific formula of the gain matrix G is:
G＝HTR-1H；
wherein: g is a gain matrix; h is a first-order Jacobian matrix; r-1Is the inverse of the measurement weight matrix;
the specific formula of the iteration quantity is as follows:
wherein:
for the amount of iteration of each state vector, HTA transposed matrix which is a first-order Jacobian matrix; r-1Is the inverse of the measurement weight matrix; z is a state estimation function;
a measurement function corresponding to each state vector.
Further, the carbon emission evaluation index in the fourth step includes a total power carbon emission index CEQ, a power carbon emission intensity index CEI, and a zero carbon power ratio index NCI.
The power carbon emission total index is a measure of the total carbon emission generated by regional societal power supply in a certain period of time. The power carbon emission intensity index is a unit carbon emission representing regional social electric energy supply in a certain period of time. The zero-carbon electric energy ratio index represents the generated energy ratio of net zero-carbon energy in the whole social electric energy supply in a certain period of time. And (4) uniformly analyzing the three indexes to comprehensively analyze the carbon emission condition.
Further, the calculation formula of the total carbon emission power index CEQ is as follows:
the calculation formula of the power carbon emission intensity index CEI is as follows:
the formula for calculating the zero-carbon electric energy ratio index NCI is as follows:
wherein: n is the quantity of electric quantity components of the electric quantity consumed by the whole social caliber in the t period area; n is a radical ofCThe quantity of the electric quantity components containing the discharge quantity of the carbon emission in the t-period region; ci.tThe unit carbon emission corresponding to the total social caliber consumption electricity of the ith electricity component in the t-period region; qi.tThe power consumption of the i-th power component in the t-period region is the power consumption of the whole social caliber; qci.tThe electric quantity of the i-th carbon-containing emission electric quantity component in the t period region.
Further, Qi.tThe method comprises the self electricity generation quantity of a generator set in an area and the quantity of electricity received by the outside of the area.
Further, the energy data includes electrical data and energy carbon emission data, the electrical data includes voltage data, current data and phase angle data of each generator end in the area needing power carbon emission evaluation, and the energy carbon emission data includes carbon content data of fuel consumed in the area needing power carbon emission evaluation and a unit electricity carbon emission value of each unit in the area.
Further, when the optimized energy data is classified in the third step, the classification categories of the optimized energy data include a power and electric quantity data type, a primary energy data type and a carbon emission detection data type.
Energy data are classified, electric quantity components are distinguished through data categories, the electric quantity components can be distinguished accurately, and accuracy of subsequent evaluation of electric power carbon emission conditions is improved.
Further, in the third step, the electric quantity components comprise the electric quantity generated by the thermal power generating unit and the electric quantity generated by the zero-carbon emission unit.
The power carbon emission assessment system based on the energy data comprises a data acquisition module, a data processing module and a data analysis module, wherein the data acquisition module is connected with the data processing module, the data acquisition module is used for acquiring the energy data, the data processing module is used for optimizing the energy data through state estimation, the data analysis module is connected with the data processing module, and the data analysis module is used for assessing the power carbon emission according to the optimized energy data.
The invention has the beneficial effects that:
replace traditional electric power electric quantity data, coal-fired gas data and discharge gas detection data through energy data, only can accomplish energy data's collection through power dispatching control center, do not need to carry out data acquisition through the collection system of difference, can unify the data type, reduce the data processing degree of difficulty, still optimize energy data through the weighted least square method after gathering energy data, when guaranteeing follow-up carbon emission evaluation index and calculating, the result that obtains is more accurate, and then improves the aassessment accuracy of electric power carbon emission. And the region and the time period are limited when carbon emission evaluation is carried out, so that the influence of seasonal fluctuation on the carbon emission evaluation can be reduced, and the accuracy of the carbon emission evaluation result is improved. And through accurate assessment of the power carbon emission conditions in the limited area and the time period, the construction result of clean low carbon of energy in the limited area can be shown through the assessment result, an optimization basis is provided for the optimization of subsequent clean low carbon work of energy, and the calculation formula of the assessment index used for low carbon assessment can be suitable for calculation on different time scales, so that the power carbon emission assessment system can monitor the power carbon emission conditions in real time, the whole development trend of the power carbon emission conditions can be assessed, and the application range is wider.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a schematic diagram of an embodiment of the present invention;
FIG. 3 is a schematic illustration of an annual analysis of power carbon emissions during the year 2020 of a zone 2016-;
FIG. 4 is a schematic diagram of monthly analysis of electric power carbon emission in 2019 and 2020 in a certain region according to an embodiment of the present invention;
FIG. 5 is a graph of daily analysis data of 2 month electrical carbon emission in a certain area in a year according to an embodiment of the present invention;
wherein: 1. the device comprises a data acquisition module 2, a data processing module 3 and a data analysis module.
Detailed Description
The invention is further described below with reference to the figures and examples.
Example (b):
an electric power carbon emission assessment method based on energy data, as shown in fig. 1, comprises the following steps:
the method comprises the steps that firstly, an electric power carbon emission evaluation system receives an evaluation request of electric power carbon emission and determines an area and a time period which need to be evaluated according to the evaluation request, a data acquisition module 1 acquires energy data of the determined area and the determined time period which need to be evaluated, and the data acquisition module 1 transmits the acquired energy data to a data processing module 2;
secondly, the data processing module 2 carries out state estimation processing on the energy data through a weighted least square method, so that energy data optimization is carried out;
classifying the optimized energy data by the data analysis module 3, and acquiring all electric quantity components contained in the electric quantity consumed in the area and the time period required to be subjected to electric power carbon emission evaluation according to the classification result;
and step four, the data analysis module 3 calculates the carbon emission evaluation indexes in the areas and time periods which need to be subjected to power carbon emission evaluation according to the electric quantity components and the optimized energy data, and the data analysis module 3 obtains the power carbon emission evaluation result according to the calculation result of the carbon emission evaluation indexes.
In the second step, the data processing module 2 performs state estimation processing on the energy data by a weighted least square method, and the specific steps are as follows:
2.1, establishing a state estimation function z of energy data;
2.2, determining a measurement weight matrix R of each state vector x in the state estimation function;
2.3, calculating a first-order Jacobian matrix H of the state estimation function;
2.4, calculating according to the measurement weight matrix R and the first-order Jacobian matrix H to obtain a gain matrix G;
2.5, calculating the iteration quantity delta x of each state vector x, judging whether the iteration quantity delta x is smaller than a rated precision threshold value, and if so, acquiring optimized energy data, namely x + delta x; otherwise, returning to the step 2.1 to recalculate until the iteration quantity Δ x of each state vector x is smaller than the rated precision threshold.
The specific formula of the state estimation function z is as follows:
z＝h(x)+e；
wherein: z is a state estimation function;
is a state vector, U is a voltage amplitude, I is a current amplitude,
is a phase angle, and w is a unit electric quantity carbon emission value of the unit; e is the measurement error; h (x) is a measurement function of the state vector;
the specific formula of the first-order Jacobian matrix H is as follows:
wherein: h is a first-order Jacobian matrix, U is a voltage amplitude, and I is electricityThe magnitude of the flow is such that,
is a phase angle, and w is a unit electric quantity carbon emission value of the unit; h (U) is a measure of the voltage amplitude U, h (I) is a measure of the current amplitude I,
is a phase angle
H (w) is a measurement function of the unit electric quantity carbon emission value w of the unit;
the specific formula of the gain matrix G is:
G＝HTR-1H；
wherein: g is a gain matrix; h is a first-order Jacobian matrix; r-1Is the inverse of the measurement weight matrix;
the specific formula of the iteration quantity is as follows:
wherein:
for the amount of iteration of each state vector, HTA transposed matrix which is a first-order Jacobian matrix; r-1Is the inverse of the measurement weight matrix; z is a state estimation function;
The carbon emission evaluation index in the fourth step comprises a power carbon emission total index CEQ, a power carbon emission intensity index CEI and a zero carbon electric energy ratio index NCI.
The calculation formula of the power carbon emission total quantity index CEQ is as follows:
the calculation formula of the power carbon emission intensity index CEI is as follows:
the formula for calculating the zero-carbon electric energy ratio index NCI is as follows:
wherein: n is the quantity of electric quantity components of the electric quantity consumed by the whole social caliber in the t period area; n is a radical ofCThe quantity of the electric quantity components containing the discharge quantity of the carbon emission in the t-period region; ci.tThe unit carbon emission corresponding to the total social caliber consumption electricity of the ith electricity component in the t-period region; qi.tThe power consumption of the i-th power component in the t-period region is the power consumption of the whole social caliber; qci.tThe electric quantity of the i-th carbon-containing emission electric quantity component in the t period region.
The total power carbon emission index CEQ can effectively reflect the condition of power carbon peak in the region, the power carbon emission intensity index CEI can provide trend information of the carbon peak, the zero-carbon electric energy ratio index NCI can effectively depict the power utilization cleaning level in the region, and the three cooperate with each other to effectively analyze and evaluate the condition of power carbon emission in the region.
Qi.tThe method comprises the self electricity generation quantity of a generator set in an area and the quantity of electricity received by the outside of the area.
The energy data comprises electric data and energy carbon emission data, the electric data comprises voltage data, current data and phase angle data of the generator end in the region needing power carbon emission assessment, and the energy carbon emission data comprises carbon content data of fuel consumed in the region needing power carbon emission assessment and unit electric quantity carbon emission values of units in the region.
And in the third step, when the optimized energy data are classified, the classification categories of the optimized energy data comprise the electric power and electric quantity data type, the primary energy data type and the carbon emission detection data type.
And in the third step, the electric quantity components comprise the electric quantity generated by the thermal power generating unit and the electric quantity generated by the zero-carbon emission unit.
The zero-carbon emission unit comprises a hydroelectric power unit, a nuclear power unit, a wind power unit and other new energy source units. And the thermal power generating unit can be further classified according to the unit capacity and the unit type so as to better determine the unit electric quantity carbon emission value corresponding to each electric quantity component.
An electric power carbon emission assessment system based on energy data is shown in fig. 2 and comprises a data acquisition module 1, a data processing module 2 and a data analysis module 3, wherein the data acquisition module 1 is connected with the data processing module 2, the data acquisition module 1 is used for acquiring the energy data, the data processing module 2 is used for optimizing the energy data through state estimation, the data analysis module 3 is connected with the data processing module 2, and the data analysis module 3 is used for carrying out electric power carbon emission assessment according to the optimized energy data.
The power carbon emission conditions of 2016-2020 year are evaluated and analyzed in a certain region by taking the year as a time period, and the evaluation result is shown in fig. 3, wherein the total annual power carbon emission in the region is gradually increased year by year, reaches the maximum value of 26063 ten thousand tons in 2018 year, and is stabilized at 26000 ten thousand tons in 2019 and 2020. Although there was an increase in intensity, the carbon emission intensity was decreasing gradually, with 532.94 g/kw at 2020, which was a 13.87% decrease over 2016. It can be seen that the electrical carbon emission in this area is moving towards cleaner.
The power carbon emission conditions of 2019 and 2020 in a certain region are accurately analyzed in a monthly time period, and the evaluation result is shown in fig. 4, and the power carbon emission intensity of 10 months in 2020 is lower than that of 2019 except for 5 months and 11 months.
The power carbon emission situation of 2 months in a year in a certain area is evaluated by taking a day as a time period, as shown in fig. 5, the total carbon emission situation is generally similar to the aperture electricity quantity increase situation of the whole society, and as can be seen from the graph during the spring festival in the 2 months in the year, the load is lower during the holiday period of the spring festival, and the total carbon emission is less; after the spring festival, the total carbon emission amount gradually increases along with the load of the whole network rising again. Because the load is lower during the spring festival, the new energy power generation accounts for higher proportion of the whole network power generation, the zero carbon electric energy accounts for higher than the NCI index as a whole, and the zero carbon electric energy accounts for higher than the NCI index generally and is maintained at 40 percent. In 16 days after 2 months, the weather is mainly rainy, so the photovoltaic power generation is low, the photovoltaic power generation of the whole society is only 336 ten thousand kilowatts, and the NCI is slightly low in relation to the previous days and the later days and is 40.61%. And 2, 19 days in 2 months, the holiday period of the spring festival is over, the time for reworking of part of enterprises is short, the power consumption demand is increased rapidly, although the weather is sunny and the total photovoltaic power generation amount is high, the output of the general-adjustment coal-fired unit is the highest in days before and after, and reaches 1914 ten thousand kilowatts, and the NCI is slightly lower in days before and after, and is 39.41%.
From the analysis result, the power carbon emission index in the area can be accurately analyzed through the energy data, and meanwhile, the development condition of the power carbon emission condition in the area can be judged according to the power carbon emission index, so that an optimization basis is provided for low-carbon emission reduction work.
The above-described embodiments are only preferred embodiments of the present invention, and are not intended to limit the present invention in any way, and other variations and modifications may be made without departing from the spirit of the invention as set forth in the claims.
Claims (10)
1. An electric power carbon emission assessment method based on energy data is characterized by comprising the following steps:
the method comprises the steps that firstly, an electric power carbon emission evaluation system receives an evaluation request of electric power carbon emission and determines an area and a time period which need to be evaluated according to the evaluation request, a data acquisition module (1) acquires energy data of the determined area and the determined time period which need to be evaluated, and the data acquisition module (1) transmits the acquired energy data to a data processing module (2);
secondly, the data processing module (2) performs state estimation processing on the energy data through a weighted least square method, so that energy data optimization is performed;
classifying the optimized energy data by the data analysis module (3), and acquiring all electric quantity components contained in the electric quantity consumed in the area and the time period required to be subjected to electric power carbon emission evaluation according to the classification result;
and step four, the data analysis module (3) calculates carbon emission evaluation indexes in areas and time periods which need to be subjected to power carbon emission evaluation according to the electric quantity components and the optimized energy data, and the data analysis module (3) obtains power carbon emission evaluation results according to calculation results of the carbon emission evaluation indexes.
2. The method for evaluating carbon emission from electric power based on energy data as claimed in claim 1, wherein the step two in which the data processing module (2) performs the state estimation processing on the energy data by a weighted least square method comprises the specific steps of:
2.1, establishing a state estimation function z of energy data;
2.2, determining a measurement weight matrix R of each state vector x in the state estimation function;
2.3, calculating a first-order Jacobian matrix H of the state estimation function;
2.4, calculating according to the measurement weight matrix R and the first-order Jacobian matrix H to obtain a gain matrix G;
2.5, calculating the iteration quantity delta x of each state vector x, judging whether the iteration quantity delta x is smaller than a rated precision threshold value, and if so, acquiring optimized energy data, namely x + delta x; otherwise, returning to the step 2.1 to recalculate until the iteration quantity Δ x of each state vector x is smaller than the rated precision threshold.
3. The method for evaluating carbon emission from electric power based on energy data as claimed in claim 2, wherein the specific formula of the state estimation function z is:
z＝h(x)+e；
wherein: z is state estimationA function;
is a state vector, U is a voltage amplitude, I is a current amplitude,
is a phase angle, and w is a unit electric quantity carbon emission value of the unit; e is the measurement error; h (x) is a measurement function of the state vector;
the specific formula of the first-order Jacobian matrix H is as follows:
wherein: h is a first-order Jacobian matrix, U is a voltage amplitude, I is a current amplitude,
is a phase angle, and w is a unit electric quantity carbon emission value of the unit; h (U) is a measure of the voltage amplitude U, h (I) is a measure of the current amplitude I,
is a phase angle
H (w) is a measurement function of the unit electric quantity carbon emission value w of the unit;
the specific formula of the gain matrix G is:
G＝HTR-1H；
wherein: g is a gain matrix; h is a first-order Jacobian matrix; r-1Is the inverse of the measurement weight matrix;
the specific formula of the iteration quantity is as follows:
wherein:
for the amount of iteration of each state vector, HTA transposed matrix which is a first-order Jacobian matrix; r-1Is the inverse of the measurement weight matrix; z is a state estimation function;
a measurement function corresponding to each state vector.
4. The method of claim 1, wherein the carbon emission assessment index in step four comprises a total power carbon emission index (CEQ), a power carbon emission intensity index (CEI) and a zero carbon to electrical energy ratio index (NCI).
5. The method as claimed in claim 4, wherein the total CEQ is calculated by the following formula:
the calculation formula of the power carbon emission intensity index CEI is as follows:
the formula for calculating the zero-carbon electric energy ratio index NCI is as follows:
wherein: electric quantity of all-social caliber consumed electric quantity in time period region with N as tFractional amount; n is a radical ofCThe quantity of the electric quantity components containing the discharge quantity of the carbon emission in the t-period region; ci.tThe unit carbon emission corresponding to the total social caliber consumption electricity of the ith electricity component in the t-period region; qi.tThe power consumption of the i-th power component in the t-period region is the power consumption of the whole social caliber; qci.tThe electric quantity of the i-th carbon-containing emission electric quantity component in the t period region.
6. The method according to claim 5, wherein Q is Qi.tThe method comprises the self electricity generation quantity of a generator set in an area and the quantity of electricity received by the outside of the area.
7. The method according to claim 1, wherein the energy data includes electrical data and energy carbon emission data, the electrical data includes voltage data, current data and phase angle data of each generator end in the area where the estimation of the carbon emission of electricity is required, and the energy carbon emission data includes carbon content data of fuel consumed in the area where the estimation of the carbon emission of electricity is required and carbon emission value per unit electricity of each unit in the area.
8. The method according to claim 1, wherein the classification of the optimized energy data in the step three comprises a power capacity data type, a primary energy data type and a carbon emission detection data type.
9. The method according to claim 1, wherein the electric power carbon emission assessment method based on energy data is characterized in that the electric power component in step three comprises electric power generation of a thermal power generating unit and electric power generation of a zero-carbon emission unit.
10. The electric power carbon emission assessment system based on the energy data is characterized by comprising a data acquisition module (1), a data processing module (2) and a data analysis module (3), wherein the data acquisition module (1) is connected with the data processing module (2), the data acquisition module (1) is used for acquiring the energy data, the data processing module (2) is used for optimizing the energy data through state estimation, the data analysis module (3) is connected with the data processing module (2), and the data analysis module (3) is used for carrying out electric power carbon emission assessment according to the optimized energy data.
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