Air-conditioner (AC) accounts for a significant share of residential energy consumption. Considering the widespread rise in electric vehicle (EV) usage, its charging would also contribute a considerable percentage of consumer's total energy consumption. Consequently, the concurrent operation of AC and EV charging would result in peaky load curves. Hence, this study proposes a system of agents for AC and EV charging applications, which incorporates load-management strategies to flatten the load curve. Thereby, the presented system includes two agents, namely: a smart load node for a thermostatically controlled load (SLN-TCL) and a smart battery charge controller. Subsequently, a subagent, namely micro-node, has been introduced to support SLN-TCL and to implement the concept of distributed temperature sensing (DTS). The implementation of DTS subdues the conventional temperature sensing mechanism of AC and ensures a more flexible operation. This study includes the design, development, and features of agents and subagents for AC and EV applications. Furthermore, this study also demonstrates the agent-based control actions for peak-shaving under real conditions to showcase the performance of this system.
Under Smart Grid environment, the consumers may respond to incentive-based smart energy tariffs for a particular consumption pattern. Demand Response (DR) is a portfolio of signaling schemes from the utility to the consumers for load shifting/shedding with a given deadline. The signaling schemes include Time-of-Use (ToU) pricing, Maximum Demand Limit (MDL) signals etc. This paper proposes a DR algorithm which schedules the operation of home appliances/loads through a minimization problem. The category of loads and their operational timings in a day have been considered as the operational parameters of the system. These operational parameters determine the dynamic priority of a load, which is an intermediate step of this algorithm. The ToU pricing, MDL signals, and the dynamic priority of loads are the constraints in this formulated minimization problem, which yields an optimal schedule of operation for each participating load within the consumer provided duration. The objective is to flatten the daily load curve of a smart home by distributing the operation of its appliances in possible low-price intervals without violating the MDL constraint. This proposed algorithm is simulated in MATLAB environment against various test cases. The obtained results are plotted to depict significant monetary savings and flattened load curves.
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