Search results for "Virtual Power Plant"
showing 3 items of 3 documents
Integration of distributed energy systems in micro-grid architecture for making a virtual power plant
2015
This paper presents a novel concept for increasing the penetration level of distributed renewable energy systems into the main electricity grid. When increasing the renewable energy penetration, it is important to implement the frequency based power delivery in distributed generators and work as traditional synchronous generators. This can be achieved by improving the power processing unit of each renewable generation units to work as active generators. But in existing grid architecture, the grid frequency is controlled as one common variable over the electricity grid. With such a method, it is difficult to use frequency based power sharing in small distributed generators and participate in…
Enabling peer-to-peer User-Preference-Aware Energy Sharing Through Reinforcement Learning
2020
Renewable, heterogeneous and distributed energy resources are the future of power systems, as envisioned by the recent paradigm of Virtual Power Plants (VPPs). Residential electricity generation, e.g., through photovoltaic panels, plays a fundamental role in this paradigm, where users are able to participate in an energy sharing system and exchange energy resources among each other. In this work, we study energy sharing systems and, differently from previous approaches, we consider realistic user behaviors by taking into account the user preferences and level of engagement in the energy trades. We formulate the problem of matching energy resources while contemplating the user behavior as a …
A Reinforcement Learning Approach for User Preference-aware Energy Sharing Systems
2021
Energy Sharing Systems (ESS) are envisioned to be the future of power systems. In these systems, consumers equipped with renewable energy generation capabilities are able to participate in an energy market to sell their energy. This paper proposes an ESS that, differently from previous works, takes into account the consumers’ preference, engagement, and bounded rationality. The problem of maximizing the energy exchange while considering such user modeling is formulated and shown to be NP-Hard. To learn the user behavior, two heuristics are proposed: 1) a Reinforcement Learning-based algorithm, which provides a bounded regret and 2) a more computationally efficient heuristic, named BPT- ${K}…