CN110837983A - Multi-scenario demand response strategy considering distributed power supply, energy storage device and hybrid load - Google Patents
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Abstract
The invention discloses a multi-scenario demand response strategy considering a distributed power supply, an energy storage device and a mixed load, which simulates the strategy implementation situation for many times through the selection of interval parameters, thereby finding out a relatively excellent incentive level as far as possible within the participation rate range allowed by a DR strategy, ensuring that a scheduling strategy can achieve a better effect under the condition of a lower incentive level, and improving the strategy applicability. And the user realizes a shallow interaction form with the power grid side through the uncertainty of the participation response. And the power grid side further influences the response degree of the whole user to the scheduling strategy by controlling the excitation level.
Description
Technical Field
The invention relates to a multi-scenario demand response strategy considering a distributed power supply, an energy storage device and a hybrid load.
Background
Demand side response (DR) means that a power consumer changes its inherent electricity usage habit according to an economic compensation policy or a price incentive means issued by a power distribution network, and the purposes of reducing the operation cost of the power distribution network, cutting peaks and filling valleys and the like are achieved by shifting electricity usage periods or cutting off electricity usage loads. The demand side response can effectively cope with the fluctuation and uncertainty of the output of the intermittent distributed power source.
Demand side responses are generally classified into incentive type demand side responses and price type demand side responses. The incentive type demand side response means that direct economic compensation is given to users participating in load reduction, and the purposes of peak clipping and valley filling are achieved. The price type demand side response means that a method such as time-of-use electricity price is adopted to guide a user to autonomously adjust the electricity utilization behavior of the user, and load of a part of high peak time period is transferred to a low valley time period, so that peak clipping and valley filling of the load are realized.
According to the psychological principle of consumers, the incentive policy given by the power grid has a difference threshold value and a saturation value for the stimulation of each user. For the whole community users, under different incentive levels, the number of people who actually participate in the strategy also has theoretical upper limit and lower limit (namely, an uncertain interval exists). For a single user, the uncertainty is represented by whether to respond to the DR strategy and the load reduction proportion of the response; for all users in the intelligent electricity utilization cell, the uncertainty can be expressed as the DR participation rate (i.e. the number of users participating in the response is the percentage of the total number of users, which receives the scheduling policy) of the whole cell.
Disclosure of Invention
The invention aims to provide a multi-scenario demand response strategy which takes distributed power supplies, energy storage devices and hybrid loads into consideration and achieves a better effect and improves the applicability of the strategy.
The technical solution of the invention is as follows:
a multi-scenario demand response strategy considering distributed power supplies, energy storage devices and hybrid loads is characterized in that: the method comprises the following steps:
according to the user load reduction response curve model, establishing a linear model approximation to represent the behavior of the user participating in the response;
the incentive level is expressed by discount of electricity fee, and at a certain incentive level x, the upper and lower bounds of the response participation rate can be respectively expressed as an expression (3-68) and an expression (3-69);
in the formula, PRup(x) Upper limit function, PR, representing response participation ratedown(x) A lower bound function representing a response engagement rate; x is the number of0Representing the critical excitation level at the critical point x0In the past, the participation rate is expressed as a dead zone, namely the motivation level cannot reach the degree that the user integrally responds to the strategy; x is the number of1Represents the ideal saturation excitation level when the excitation level reaches x1When the participation rate reaches an ideal saturation point, namely the upper limit of the participation rate just reaches 100 percent; x is the number of2Indicating a fully saturated excitation level when the excitation level reaches x2When the participation rate reaches the complete saturation point, namely the uncertain fluctuation interval of the participation rate disappears, and the participation rate becomes the determined quantity of 100 percent;
considering that the incentive level is positively correlated with the cost of the power grid side, and combining with the probability statistics theory, the optimal participation rate PRoptUnder-policy optimal excitation value xoptCan be expressed as formula (3-70);
combining the formula (3-68) with the formula (3-6), the formula (3-70) can be converted into the formula (3-71);
in the formula, PRoptRepresenting the optimal participation rate; x is the number ofminAnd xmaxRespectively expressed at the optimum participation rate PRoptLower excitation horizontal interval [ x ]min，xmax]Interval minimum and maximum of (c); x is the number ofoptExpressed at the optimum participation rate PRoptThe optimal excitation level of.
The demand response model of the invention adopts the random number in a certain interval to represent the uncertain behavior of the participation of the user in response, and the strategy implementation situation is simulated for a plurality of times through the selection of the interval parameters, so that a relatively excellent incentive level is found within the participation rate range allowed by the DR strategy as far as possible, the scheduling strategy can achieve a better effect under the condition of a lower incentive level, and the strategy applicability is improved. And the user realizes a shallow interaction form with the power grid side through the uncertainty of the participation response. And the power grid side further influences the response degree of the whole user to the scheduling strategy by controlling the excitation level.
Drawings
The invention is further explained below by referring to the drawings and examples.
FIG. 1 is a schematic diagram of a user response engagement rate curve accounting for uncertainty.
Detailed Description
A multi-scenario demand response strategy considering distributed power, energy storage, and hybrid loads, comprising:
according to the user load reduction response curve model, establishing a linear model approximation to represent the behavior of the user participating in the response;
the incentive level is expressed by discount of electricity fee, and at a certain incentive level x, the upper and lower bounds of the response participation rate can be respectively expressed as an expression (3-68) and an expression (3-69);
in the formula, PRup(x) Upper limit function, PR, representing response participation ratedown(x) A lower bound function representing a response engagement rate; x is the number of0Representing the critical excitation level at the critical point x0In the past, the participation rate is expressed as a dead zone, namely the motivation level cannot reach the degree that the user integrally responds to the strategy; x is the number of1Represents the ideal saturation excitation level when the excitation level reaches x1When the participation rate reaches an ideal saturation point, namely the upper limit of the participation rate just reaches 100 percent; x is the number of2Indicating a fully saturated excitation level when the excitation level reaches x2When the participation rate reaches the complete saturation point, namely the uncertain fluctuation interval of the participation rate disappears, and the participation rate becomes the determined quantity of 100 percent;
considering that the incentive level is positively correlated with the cost of the power grid side, and combining with the probability statistics theory, the optimal participation rate PRoptUnder-policy optimal excitation value xoptCan be expressed as formula (3-70);
combining the formula (3-68) with the formula (3-6), the formula (3-70) can be converted into the formula (3-71);
in the formula, PRoptRepresenting the optimal participation rate; x is the number ofminAnd xmaxRespectively expressed at the optimum participation rate PRoptLower excitation horizontal interval [ x ]min，xmax]Interval minimum and maximum of (c); x is the number ofoptExpressed at the optimum participation rate PRoptOptimum excitation level of。
Claims (1)
1. A multi-scenario demand response strategy considering distributed power supplies, energy storage devices and hybrid loads is characterized in that: the method comprises the following steps:
according to the user load reduction response curve model, establishing a linear model approximation to represent the behavior of the user participating in the response;
the incentive level is expressed by discount of electricity fee, and at a certain incentive level x, the upper and lower bounds of the response participation rate can be respectively expressed as an expression (3-68) and an expression (3-69);
in the formula, PRup(x) Upper limit function, PR, representing response participation ratedown(x) A lower bound function representing a response engagement rate; x is the number of0Representing the critical excitation level at the critical point x0In the past, the participation rate is expressed as a dead zone, namely the motivation level cannot reach the degree that the user integrally responds to the strategy; x is the number of1Represents the ideal saturation excitation level when the excitation level reaches x1When the participation rate reaches an ideal saturation point, namely the upper limit of the participation rate just reaches 100 percent; x is the number of2Indicating a fully saturated excitation level when the excitation level reaches x2When the participation rate reaches the complete saturation point, namely the uncertain fluctuation interval of the participation rate disappears, and the participation rate becomes the determined quantity of 100 percent;
considering that the incentive level is positively correlated with the cost of the power grid side, and combining with the probability statistics theory, the optimal participation rate PRoptUnder-policy optimal excitation value xoptCan be expressed as formula (3-70);
combining the formula (3-68) with the formula (3-6), the formula (3-70) can be converted into the formula (3-71);
in the formula, PRoptRepresenting the optimal participation rate; x is the number ofminAnd xmaxRespectively expressed at the optimum participation rate PRoptLower excitation horizontal interval [ x ]min，xmax]Interval minimum and maximum of (c); x is the number ofoptExpressed at the optimum participation rate PRoptThe optimal excitation level of.
The demand response model adopts random numbers in a certain interval to represent the uncertain behavior of the participation of the user in response, and the strategy implementation condition is simulated for many times through the selection of interval parameters, so that a relatively excellent incentive level is found within the participation rate range allowed by the DR strategy as far as possible, the scheduling strategy can achieve a better effect under the condition of a lower incentive level, and the strategy applicability is improved. And the user realizes a shallow interaction form with the power grid side through the uncertainty of the participation response. And the power grid side further influences the response degree of the whole user to the scheduling strategy by controlling the excitation level.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113256031A (en) * | 2021-06-25 | 2021-08-13 | 国网江西省电力有限公司供电服务管理中心 | Self-learning optimization method based on resident demand response strategy |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103679357A (en) * | 2013-12-06 | 2014-03-26 | 国网山东省电力公司 | Power demand response intelligent decision method based on price and excitation |
| CN110210647A (en) * | 2019-04-29 | 2019-09-06 | 国网江苏省电力有限公司电力科学研究院 | A kind of distributed generation resource, energy storage and flexible load combined scheduling method and device |
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103679357A (en) * | 2013-12-06 | 2014-03-26 | 国网山东省电力公司 | Power demand response intelligent decision method based on price and excitation |
| CN110210647A (en) * | 2019-04-29 | 2019-09-06 | 国网江苏省电力有限公司电力科学研究院 | A kind of distributed generation resource, energy storage and flexible load combined scheduling method and device |
Non-Patent Citations (1)
| Title |
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| 彭文昊等: "计及用户参与不确定性的需求响应策略优化方法", 《电网技术》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113256031A (en) * | 2021-06-25 | 2021-08-13 | 国网江西省电力有限公司供电服务管理中心 | Self-learning optimization method based on resident demand response strategy |
| CN113256031B (en) * | 2021-06-25 | 2021-10-26 | 国网江西省电力有限公司供电服务管理中心 | Self-learning optimization method based on resident demand response strategy |
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