Dynamic Residential Demand Response and Distributed Generation Management in Smart Microgrid with Hierarchical Agents
Bingnan Jiang, Yunsi Fei
Abstract
Bingnan Jiang, Yunsi Fei
Abstract
Smart grid has been a significant development trend of power system. Within smart grid, microgrids share the burden of traditional grids, reduce energy consumption cost and alleviate environment deterioration. This paper proposes a dynamic Demand Response (DR) and Distributed Generation (DG) management approach in the context of smart microgrid for a residential community. With a dynamic update mechanism, the DR operates automatically and allows manual interference. The DG management coordinates with DR and considers stochastic elements, such as stochastic load and wind power, to reduce the energy consumption cost of the community. Simulation and numerical results show the effectiveness of the system on reducing the energy consumption cost while keeping users’ satisfaction at a high level.
OpenAlex reports 85 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
Smart grid has been a significant development trend of power system. Within smart grid, microgrids share the burden of traditional grids, reduce energy consumption cost and alleviate environment deterioration. This paper proposes a dynamic Demand Response (DR) and Distributed Generation (DG) management approach in the context of smart microgrid for a residential community. With a dynamic update mechanism, the DR operates automatically and allows manual interference. The DG management coordinates with DR and considers stochastic elements, such as stochastic load and wind power, to reduce the energy consumption cost of the community. Simulation and numerical results show the effectiveness of the system on reducing the energy consumption cost while keeping users’ satisfaction at a high level.
Key concepts: Microgrid, Smart grid, Demand response, Context (archaeology), Distributed generation, Computer science, Energy management, Consumption (sociology)