Integration of OpenADR with Node-RED for Demand Response Load Control Using Internet of Things Approach

Generalized Nash equilibrium problem based electric vehicle charging management in distribution networks

“…This minimizer ν 0 t c represents a direction towards the charging cost reduction. Due to the convexity of strategy set M μ t c À Á , we update a new fixed point through Equation 14 and guarantee that the new fixed point μ 1 t c is also in the feasible strategy set M μ t c À Á , where λ k ∈ (0, 1) is a step coefficient.…”

“…Repeat this process and keep updating μ m t c and ν m t c as Equation 14 until the value of V μ t c À Á sufficiently approaches zero. When the updated Nikaido-Isoda function value drops below a preset stopping tolerance ε, we consider the updated fixed point μ m t c is near the GNE of the PEV charging GNEP or called ε-GNE.…”

“…Cao et al introduce a real‐time hardware‐in‐the loop approach to develop and verify charging scheduling schemes, which coordinates PEV charging with building loads and the renewable energy generation. Aggarwal et al states a demand response‐based PEV charging control in single board computer platform. Gong et al develop an optimal PEV charging control strategy to reduce the distribution transformer loss due to the aggregated PEV charging load.…”

Application of DR and co‐simulation approach for renewable integrated HEMS: a review

“…However, the uncertainty in power generation and load profiles can be dealt at local level energy management systems [2, 3], which in turn reduces the burden of the main grid. Demand response (DR) programmes [4] have proven to be good in achieving considerable economic and technical goals in this smart grid (SG) era [5]. The interactive mutual understanding of home energy management system (HEMS) and utility EMS creates a win–win situation through different DR programmes without compromising customer flexibility [6].…”