Abstract
The price-based demand response has been considered one of the most effective ways to reduce the peak demand of power grids. However, it is possible to form a new rebound critical peak to threaten the grid stability and economic operation when large-scale loads respond to the price signal simultaneously. Based on the Internet of Things (IoT), this paper proposes a cloud-edge coordination (CEC) automatic control strategy to enable interaction and cooperation among the power grid and massive individual air conditioners (ACs) and eliminate the grid rebound critical peak of the synchro-response. Edge computing actively provides optional cooperation electricity plans, migrates computationally intensive tasks from the cloud and guarantees the privacy of users. Considering the unreliability of network transmission, data packet dropouts (/or delays and even downtime) are inevitable and usually random in the transmission, and a dual-feedback closed-loop control is first proposed in this paper. Finally, the effectiveness of the optimized closed-loop control strategy is verified by simulation cases.
Highlights
DR programs are generally classified into two categories: incentivebased demand response (IBDR) and price-based demand response (PBDR) [1], [2]
We propose a cloud-edge coordination (CEC) automatic control strategy based on cooperative game theory to enable interactions and cooperation among the power grid and numerous air conditioners (ACs)
Based on cooperative game theory, the key idea of the CEC control strategy is that the cloud computing center only decides how to cooperate to reduce the critical peak, which corresponds to a negotiation to find the disagreement point (DP) and agreement point (AP) in the game
Summary
R EDUCING the peak load of the power grid can bring tremendous economic benefits. The demand response (DR) has been widely recognized as an important approach to reduce the peak load of power systems. When many customers respond to the same time-varying price signal, there is a potentially significant risk that all the loads may shift from peak time to non-peak time at the same time In this case, the objective of the DR program is to reduce demand during high price periods, an unexpected peak demand called a rebound peak may occur. Reference [16] presented a direct load control strategy based on the aggregation model for operating reserves and actively responding. Reference [30] proposed an optimal sequential dispatch strategy of ACs to mitigate the rebound effect based on the aggregation model. JIANG et al.: CLOUD-EDGE COOPERATIVE MODEL AND CLOSED-LOOP CONTROL STRATEGY propose a distributed energy demand scheduling approach based on multi-leader-follower game for Smart Building. In our previous study [31], a distributed control strategy for reducing the lead-rebound peak caused by the PBDR program is presented. Due to the limitations of the distributed algorithm, improving global performance is a challenge [32]
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