Abstract
In demand response programs, load service entity (LSE) can aggregate user as an independent entity to participate in the day ahead energy market, and complete the response target through incentive in the next day. In order to reduce the incentive cost of LSE, a differentiated incentive mechanism that consider the differences in user response flexibility is proposed in this paper. Then, a user response behavior model is established by using the long short-term memory (LSTM) network, with aim of accurately predicting users' response. Subsequently, an optimization strategy combining particle swarm (PSO) and LSTM is proposed, so that the response target can be accurately completed with low cost. Simulation experiments verified that the cost of LSE is close to the theoretical minimum, and can be reduced by 20% compared with the optimal result under unified incentive mechanism. Moreover, it also verified that the proposed strategy has high response accuracy and good stability.
Highlights
With the massive penetration of new energy power generation, the flexibility of the generation is gradually decreasing
According to the International Energy Agency (IEA), the demand response potential usually accounts for about 15% of the peak demand
In PJM, distributed small users can participate in the demand response of the electricity market through the load service entity (LSE)
Summary
With the massive penetration of new energy power generation, the flexibility of the generation is gradually decreasing. In PJM, distributed small users can participate in the demand response of the electricity market through the load service entity (LSE). Reference [11] proposed a dynamic energy management framework based on highly-resolved personal energy consumption models, to re-shape the aggregate demand In these studies, the load information of each appliance of the user needs to be obtained. Based on the above analysis, demand response incentive strategies that consider user differences under incomplete information conditions need to be further studied: 1) When modeling the user’s response behavior, most of the existing research established a static model, which is independent of time. All the above methods can be used to calculate the user’s baseline load, so as to calculate the user’s actual response
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