152 research outputs found
Energy Storage Sharing Strategy in Distribution Networks Using Bi-level Optimization Approach
In this paper, we address the energy storage management problem in
distribution networks from the perspective of an independent energy storage
manager (IESM) who aims to realize optimal energy storage sharing with
multi-objective optimization, i.e., optimizing the system peak loads and the
electricity purchase costs of the distribution company (DisCo) and its
customers. To achieve the goal of the IESM, an energy storage sharing strategy
is therefore proposed, which allows DisCo and customers to control the assigned
energy storage. The strategy is updated day by day according to the system
information change. The problem is formulated as a bi-level mathematical model
where the upper level model (ULM) seeks for optimal division of energy storage
among Disco and customers, and the lower level models (LLMs) represent the
minimizations of the electricity purchase costs of DisCo and customers.
Further, in order to enhance the computation efficiency, we transform the
bi-level model into a single-level mathematical program with equilibrium
constraints (MPEC) model and linearize it. Finally, we validate the
effectiveness of the strategy and complement our analysis through case studies
Activity Detection And Modeling Using Smart Meter Data: Concept And Case Studies
Electricity consumed by residential consumers counts for a significant part
of global electricity consumption and utility companies can collect
high-resolution load data thanks to the widely deployed advanced metering
infrastructure. There has been a growing research interest toward appliance
load disaggregation via nonintrusive load monitoring. As the electricity
consumption of appliances is directly associated with the activities of
consumers, this paper proposes a new and more effective approach, i.e.,
activity disaggregation. We present the concept of activity disaggregation and
discuss its advantage over traditional appliance load disaggregation. We
develop a framework by leverage machine learning for activity detection based
on residential load data and features. We show through numerical case studies
to demonstrate the effectiveness of the activity detection method and analyze
consumer behaviors by time-dependent activity modeling. Last but not least, we
discuss some potential use cases that can benefit from activity disaggregation
and some future research directions.Comment: 2020 IEEE Power & Energy Society General Meetin
- …