Smart grid is advancing power grids significantly, with higher power generation efficiency, lower energy consumption cost, and better user experience. Microgrid utilizes distributed renewable energy generation to reduce the burden on utility grids. This paper proposes an energy ecosystem; a costeffective smart microgrid based on intelligent hierarchical agents with dynamic demand response (DR) and distributed energy resource (DER) management. With a dynamic update mechanism, DR automatically adapts to users' preference and varying external information. The DER management coordinates operations of micro combined heat and power systems (µCHPs), and vanadium redox battery (VRB) according to DR decisions. A twolevel shared cost-led µCHPs management strategy is proposed to reduce energy consumption cost further. VRB discharging is managed to be environment-adaptive. Simulations and numerical results show the proposed system is very effective in reducing the energy consumption cost while satisfying user's preference. Index Terms-Demand response (DR), distributed energy resources (DER), microgrid, particle swarm optimization (PSO), Q-learning, smart grid. I. INTRODUCTIONW ITH MORE electricity-consuming products coming into daily lives, such as electrical vehicles (EVs) and advanced heating, ventilation, and air conditioning systems, load demand increases dramatically and imposes significant burdens on the existing power grid. Smart grid, integrated with distributed renewable energy generation, advanced metering infrastructure, and information technologies, can cope with the impending global energy crisis and environment deterioration. To achieve high energy efficiency in smart grid, load can be shaved by demand response (DR) and distributed energy resources (DER) have to be well managed. Residential DR can be defined as reactions of users to the time-varying energy price offered by utility companies [1], where schedulable load is shifted to off-peak hours to reduce the energy consumption cost. On the other hand, DERs, including distributed generation (DG) and energy storage system, can be Manuscript

No abstract

No abstract

In this paper, we consider the algorithm proposed in recent years by Censor, Gibali and Reich, which solves split variational inequality problem, and Korpelevich’s extragradient method, which solves variational inequality problems. As our main result, we propose an iterative method for finding an element to solve a class of split variational inequality problems under weaker conditions and get a weak convergence theorem. As applications, we obtain some new weak convergence theorems by using our weak convergence result to solve related problems in nonlinear analysis and optimization.

No abstract