A dynamic demand response pricing method based on fuzzy reinforcement learning

A technology of reinforcement learning and demand response, applied in data processing applications, instruments, business, etc., can solve the uncertainty that cannot reflect the real-time dynamic market energy well, cannot reflect the complexity of demand response distribution, and has no logical pricing process To achieve the effect of improving grid reliability, reducing energy imbalance, and increasing rationality

Inactive Publication Date: 2019-03-12
SOUTH CHINA UNIV OF TECH
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Problems solved by technology

Demand response pricing models often use deterministic pricing models, such as time-of-use pricing models, which cannot well reflect the uncertainty of real-time dynamic market ene

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  • A dynamic demand response pricing method based on fuzzy reinforcement learning
  • A dynamic demand response pricing method based on fuzzy reinforcement learning
  • A dynamic demand response pricing method based on fuzzy reinforcement learning

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Embodiment Construction

[0055] The embodiments will be further described below in conjunction with the accompanying drawings.

[0056] A dynamic demand response pricing method based on fuzzy reinforcement learning, including steps:

[0057]S1. Establish a hierarchical power market model, including a fuzzy load demand response model, a load aggregator optimization model and its objective function model;

[0058] S2. Solve the model established in step S1 with a fuzzy reinforcement learning algorithm to obtain an optimal retail electricity price.

[0059] Such as figure 1 As shown, energy is sold by power producers to load aggregators at wholesale prices, and then sold by load aggregators to consumers at retail prices. The information exchanged among the three is mainly electricity purchase price and electricity consumption. Among them, the retail price information exchange and pricing decision-making mechanism between load aggregators and consumers is the dynamic load demand response pricing method...

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Abstract

The invention discloses a dynamic demand response pricing method based on fuzzy reinforcement learning. The method comprises the following steps: S1, establishing a hierarchical power market model, comprising a fuzzy load demand response model, a load aggregation quotient optimization model and an objective function model; S2, the model established in step S1 is solved by the fuzzy reinforcement learning algorithm to obtain the optimal retail price. The invention searches for the reasonable electricity price under the condition of considering the fuzzy uncertainty of the load response, Aimingat the shortcoming that the fuzzy uncertainty of load response is not taken into account in the dynamic demand response pricing model, a fuzzy load demand response model is proposed, load aggregationquotient optimization model and objective function model, A dynamic demand response pricing method based on fuzzy reinforcement learning is proposed, which not only fully considers the uncertainty ofload response, but also adapts to the dynamic power market environment and improves the computational efficiency. By optimizing the real-time optimal pricing strategy, the reliability of power systemcan be improved and the energy imbalance can be reduced.

Description

technical field [0001] The invention relates to a dynamic demand response pricing method based on fuzzy reinforcement learning. Background technique [0002] With the development of distribution network communication technology, demand side response has become an effective method to improve grid reliability and reduce energy loss because of its flexible regulation effect at the load side. Price-based demand response enables users to change their electricity consumption patterns according to real-time changes in electricity price signals to achieve the purpose of adjusting the load curve. The dynamic demand response pricing process is a decision-making process whose purpose is to find a reasonable electricity price to distribute the system's electrical energy services. Demand response pricing models often adopt definite pricing models, such as time-of-use pricing models, which cannot well reflect the uncertainty of real-time dynamic market energy. The dynamic price pricing ...

Claims

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Application Information

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IPC IPC(8): G06Q30/02G06Q10/06G06Q50/06
CPCG06Q10/06315G06Q30/0283G06Q50/06
Inventor 邱守强
Owner SOUTH CHINA UNIV OF TECH
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