CN111626505A - Power demand response implementation target decomposition method - Google Patents
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Abstract
The invention discloses a power demand response implementation target decomposition method, which comprises the following steps: determining an annual response total target of provincial power grid enterprises in a planning period, wherein the response target is 3% -5% of annual maximum power load; evaluating the demand response capability of the city according to the city load composition and the electricity utilization structure; classifying according to the supply and demand balance of the city; and carrying out total quantity decomposition on different types of cities according to the demand response capability and different decomposition principles. The method for decomposing the implementation target of the power demand response provided by the invention fully considers the regional load supply and demand balance and the difference of the response capability, decomposes the annual demand response target of the power grid enterprise in the planning period, is beneficial to improving the scientificity, rationality and performability of the target decomposition, and has certain guidance and reference significance for the implementation demand response of the superior power supply region organization.
Description
Technical Field
The invention relates to the technical field of power evaluation, in particular to a power demand response implementation target decomposition method.
Background
With the propulsion of atmospheric pollution prevention and control attack and hardening war, the change of power utilization structures and the explosive growth of new energy power generation, the seasonal peak load contradiction of the power grid is prominent, the peak regulation pressure is continuously increased, the demand side response potential needs to be fully excavated to solve the power supply and demand gap, and the safety and the economy of the power grid operation are effectively guaranteed. Relevant power policies specifically require provincial power grid enterprises to gradually establish the mobile peak shaving capacity of the demand side with the annual maximum power load of about 3% through demand response, and the power grid enterprises further require the planning stage to fully consider the demand response and take the demand side adjustable resources as important resources into power supply and demand balance. As a subordinate mechanism for implementing demand response by a specific organization, power supply, load development and demand side adjustable resource quantity of each city company are greatly different. In order to meet higher power supply requirements, the power supply and demand situation and the demand response capability of each region need to be balanced, and the annual demand response implementation target of provincial power grid enterprises is reasonably decomposed to cities of each region.
Disclosure of Invention
In view of the above, it is necessary to provide a power demand response implementation target decomposition method to solve the above problems.
An object of the present invention is to provide a power demand response implementation target decomposition method that is scientific, rational, and performable.
The invention provides a power demand response implementation target decomposition method, which comprises the following steps:
determining an annual response total target of provincial power grid enterprises in a planning period, wherein the response target is 3% -5% of annual maximum power load;
evaluating the demand response capability of the city according to the city load composition and the electricity utilization structure;
classifying according to the supply and demand balance of the city;
and carrying out total quantity decomposition on different types of cities according to the demand response capability and different decomposition principles.
Preferably, the formula for calculating the demand response capability of the city is:
Ai，m＝∑n＝1Di，m，n
wherein A isi，mDemand response capability for city m of year i, Di，m，nThe demand response potential of the m & ltth & gt industry of the city of the ith year.
Preferably, the formula for calculating the demand response potential of the mth industry in the ith year is as follows: di，m，n＝Li，m，n×γn；
Wherein L isi，m，nThe load prediction value of the mth industry of the city of the ith year in the peak period of power utilization of the power grid is gammanThe load proportion may be reduced for demand response periods.
Preferably, the prefecture is classified into class I prefecture, class II prefecture, class III prefecture, and class IV prefecture;
the class I city is a city without a power supply gap in a planning period and with a load scale lower than a preset value;
the class II city is a city in which a power supply gap exists in a planning period and external connecting lines are needed for supply;
the III type of city is a city with enough power supply in the early stage and the middle and later stages of the planning period;
the IV type city is a city without a power supply gap in the planning period and with the load scale larger than or equal to a preset value.
Preferably, class i cities maintain a status quo level in response to the objective during the planning period.
Preferably, the class II city is in the planning period, and the response target is consistent with the demand response capability of the region.
Preferably, the class III municipality is in planning stage, with early targets consistent with the demand response capability of the region, and middle and late targets maintained at the level of the middle of the year.
Preferably, the class iv city achieves dynamic balance of response goals and response capabilities during the planning period:
wherein Q isi，xThe reserve target is determined for the ith class of city of year i, and x is I, II, III, Ai，lThe demand response capability of the city I is the year, the city I belongs to the IV-class city, k is the balance coefficient, and the balance coefficient is more than 0 and less than or equal to 1.
Preferably, the reserve target Q for the class IV city is determined by the equilibrium factor ki，IV：Qi，IV＝k*Ai，ll∈IV。
Compared with the prior art, the method for implementing target decomposition in power demand response has the following beneficial effects:
the method for decomposing the implementation target of the power demand response provided by the invention fully considers regional load supply and demand balance and response capacity difference, decomposes the annual demand response target of a power grid enterprise in a planning period, is beneficial to improving the scientificity, rationality and performability of target decomposition, and has certain guidance and reference significance for the implementation demand response of a superior power supply region organization.
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The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
FIG. 1 is a flow diagram of a power demand response implementation goal decomposition method, according to one embodiment of the invention.
Detailed Description
The present invention will be further described with reference to the following examples. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a power demand response implementation target decomposition method which classifies cities based on partition power balance and has certain guiding and reference significance for provincial power grid enterprise organization to implement power demand response according to a differentiated city response target determination principle. Specifically, referring to fig. 1, the evaluation method includes the following steps:
and S101, determining an annual response total target of the provincial power grid enterprise in a planning period.
In a particular embodiment, the planning period is typically five years. The method of regression analysis, regional accumulation and the like is adopted to complete the annual whole provincial level power grid electricity utilization maximum load L in the planning periodi(i ═ 1, 2 … 5) prediction. The response target is 3% -5% of the annual maximum electrical load. Determining year i response target P of provincial power grid enterprise in planning periodi：Pi＝3％×Li。
And step S102, evaluating the demand response capability of the city according to the city load composition and the electricity utilization structure.
The demand response capability of the city has a great relationship with the load composition and the electricity utilization structure of the local area. There are large differences between cities: areas with a high industrial load proportion can participate in demand response by organizing non-productive loads and auxiliary production loads; areas with heavy business load can participate in demand response by organizing air conditioning and heating ventilation loads. To evaluate the demand response capability A of the city m of the ith year in the planning periodi，mThe method is obtained by summarizing the response potentials of various industries in local cities from bottom to top.
Demand response capability Ai，mThe formula of (1) is: a. thei，m＝∑n＝1Di，m，n(ii) a Wherein D isi，m，nFor the demand response potential of the mth industry of the city of the ith year, the calculation formula is as follows:
Di，m，n＝Li，m，n×γn(ii) a Wherein L isi，m，nThe load prediction value of the mth industry of the city in the ith year in the peak period of power utilization of the power grid can be analyzed in the historical years of the industryOn the basis of the electricity utilization rule, based on the industry competition situation and the regional development environment, the prediction is completed by adopting a trend extrapolation method or a Delphi method. Gamma raynThe load proportion can be reduced for the demand response time period, and the load proportion is related to the electric equipment, the production process and the load management level of the industry.
And step S103, classifying according to the supply and demand balance of the city in the future year.
Specifically, they can be classified into class I, class II, class III and class IV.
The class I city is a city without a power supply gap in a planning period and with a load scale lower than a preset value; the class II city is a city in which a power supply gap exists in a planning period and external connecting lines are needed for supply; the III-class city is a city with sufficient power supply in the early stage and in the middle and later stages of the planning period, and particularly, in the middle planning period (the t-th year, t belongs to [2, 3 and 4]) because of the newly-added span-area power-saving and power-saving input, the traditional power receiving area is converted into a power transmission area; (ii) a The IV type city is a city without a power supply gap in the planning period and with the load scale larger than or equal to a preset value. The predetermined value is the median of all the size of the city load.
And step S104, carrying out total quantity decomposition on different types of cities according to the demand response capability and different decomposition principles.
Specifically, in the class i city, in the planning period, since there is no power supply gap and the load scale is small and lower than the predetermined value, the response target maintains the current status level and does not hook the capacity of responding to the electricity in the area.
Class II cities have power supply gaps and need external tie line supply in the planning period, and the response target is consistent with the demand response capability of the region, namely Qi，m＝Ai，m. Wherein Q isi，mDemand response target for city m of year i of planning period
And in the class III city, a power supply gap exists in the early planning stage, a region with sufficient supply is provided in the middle and later stages, the early stage target of the class III city is consistent with the demand response capability of the region, and the target in the middle and later stages is maintained at the level of the middle year t.
And the IV-type city is an area in which a power supply gap gradually appears in the planning period and an area in which the power supply gap does not exist in the planning period but the load scale is larger than or equal to a preset value. Setting a balance coefficient k to realize dynamic balance of response targets and response capacity:
wherein Q isi，xThe value of x is I, II and III for the established reserve target of the x-th class city of the year i. A. thei，lFor the demand response capability of i city/year, city/is assigned to class IV city. k is an equilibrium coefficient, and k is more than 0 and less than or equal to 1.
Determination of a reserve target Q for the IV-class city by means of a balance factor ki，IV：Qi，IV＝k*Ai，ll∈IV。
The method for decomposing the implementation target of the power demand response provided by the invention fully considers the regional load supply and demand balance and the difference of the response capability, decomposes the annual demand response target of the power grid enterprise in the planning period, is beneficial to improving the scientificity, rationality and performability of the target decomposition, and has certain guidance and reference significance for the implementation demand response of the superior power supply region organization.
Thus, it should be appreciated by those skilled in the art that while a number of exemplary embodiments of the invention have been illustrated and described in detail herein, many other variations or modifications consistent with the principles of the invention may be directly determined or derived from the disclosure of the present invention without departing from the spirit and scope of the invention. Accordingly, the scope of the invention should be understood and interpreted to cover all such other variations or modifications.
Claims (9)
1. A power demand response implementation target decomposition method characterized by: the evaluation method comprises the following steps:
determining an annual response total target of provincial power grid enterprises in a planning period, wherein the response target is 3% -5% of annual maximum power load;
evaluating the demand response capability of the city according to the city load composition and the electricity utilization structure;
classifying according to the supply and demand balance of the city;
and carrying out total quantity decomposition on different types of cities according to the demand response capability and different decomposition principles.
2. The power demand response implementation target decomposition method of claim 1, characterized by: the calculation formula of the demand response capability of the city is as follows:
Ai，m＝∑n＝1Di，m，n
wherein A isi，mDemand response capability for city m of year i, Di，m，nThe demand response potential of the m & ltth & gt industry of the city of the ith year.
3. The power demand response implementation target decomposition method of claim 2, characterized by: the formula for calculating the demand response potential of the m & n & ltth & gt industry in the city of the ith year is as follows:
Di，m，n＝Li，m，n×γn；
wherein L isi，m，nThe load prediction value of the mth industry of the city of the ith year in the peak period of power utilization of the power grid is gammanThe load proportion may be reduced for demand response periods.
4. The power demand response implementation target decomposition method of claim 1, characterized by: the land cities are classified into a type I land city, a type II land city, a type III land city and a type IV land city;
the class I city is a city without a power supply gap in a planning period and with a load scale lower than a preset value;
the class II city is a city in which a power supply gap exists in a planning period and external connecting lines are needed for supply;
the III-type city is a city with sufficient power supply in the middle and later periods of the planning period;
the class IV city is a city without a power supply gap in a planning period and with a load scale larger than or equal to the preset value.
5. The power demand response implementation target decomposition method of claim 4, wherein: and the class I city maintains the current status level in response to the target in the planning period.
6. The power demand response implementation target decomposition method of claim 4, wherein: and the class II city keeps the response target consistent with the demand response capability of the region in the planning period.
7. The power demand response implementation target decomposition method of claim 4, wherein: the class III city is in the planning period, the early-stage target of the class III city is consistent with the demand response capability of the region, and the middle-stage target and the later-stage target are maintained at the level of the middle year.
8. The power demand response implementation target decomposition method of claim 4, wherein: and in the planning period, the class IV city realizes the dynamic balance of response targets and response capacity:
wherein Q isi，xThe reserve target is determined for the ith class of city of year I, and x is selected from I, II, III and Ai，lThe demand response capability of the city I is given, the city I belongs to the type IV city, k is a balance coefficient, and the balance coefficient is more than 0 and less than or equal to 1.
9. The power demand response implementation target decomposition method of claim 4, wherein: determining a reserve target Q for class IV city via the equilibrium coefficient ki，IV：
Qi，IV＝k*Ai，ll∈IV。
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150332294A1 (en) * | 2014-05-19 | 2015-11-19 | The Board Of Trustees Of The Leland Stanford Junior University | Method and system for profiling and scheduling of thermal residential energy use for demand-side management programs |
| CN106327074A (en) * | 2016-08-23 | 2017-01-11 | 国网江苏省电力公司电力科学研究院 | Method for evaluating electric power requirement response potential in province level, region/city level and area level |
| CN106602555A (en) * | 2016-12-27 | 2017-04-26 | 上海中兴电力建设发展有限公司 | System for managing energy of energy internet having hierarchical and partitioning structure |
| CN109118128A (en) * | 2018-10-30 | 2019-01-01 | 国网河南省电力公司经济技术研究院 | A kind of this area industrial enterprise electricity needs responds potential evaluation method |
| CN110766335A (en) * | 2019-10-29 | 2020-02-07 | 国网能源研究院有限公司 | Regional power planning method considering demand side response |
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150332294A1 (en) * | 2014-05-19 | 2015-11-19 | The Board Of Trustees Of The Leland Stanford Junior University | Method and system for profiling and scheduling of thermal residential energy use for demand-side management programs |
| CN106327074A (en) * | 2016-08-23 | 2017-01-11 | 国网江苏省电力公司电力科学研究院 | Method for evaluating electric power requirement response potential in province level, region/city level and area level |
| CN106602555A (en) * | 2016-12-27 | 2017-04-26 | 上海中兴电力建设发展有限公司 | System for managing energy of energy internet having hierarchical and partitioning structure |
| CN109118128A (en) * | 2018-10-30 | 2019-01-01 | 国网河南省电力公司经济技术研究院 | A kind of this area industrial enterprise electricity needs responds potential evaluation method |
| CN110766335A (en) * | 2019-10-29 | 2020-02-07 | 国网能源研究院有限公司 | Regional power planning method considering demand side response |
Non-Patent Citations (4)
| Title |
|---|
| PAUL MCNAMARA: "Hierarchical Demand Response for Peak Minimization Using Dantzig–Wolfe Decomposition", 《IEEE TRANSACTIONS ON SMART GRID》 * |
| 刘军会: "基于能耗指标的全社会用电量预测方法研究", 《河南科技》 * |
| 王静雯: "综合能源服务效用模型及用户需求评估", 《中国电机工程学报》 * |
| 田世明: "智能电网条件下的需求响应关键技术", 《中国电机工程学报》 * |
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