Related papers: Deep Reinforcement Learning Microgrid Optimization…
With the increase in awareness about the climate change, there has been a tremendous shift towards utilizing renewable energy sources (RES). In this regard, smart grid technologies have been presented to facilitate higher penetration of…
The microgrids design for remote locations represents one of the most important and critical applications of the microgrid concept. It requires the correct sizing and the proper utilization of the different sources to guarantee the…
This study aims to develop a cost-effective microgrid design that optimally balances the economic feasibility, reliability, efficiency, and environmental impact in a grid-tied community microgrid. A multi-objective optimization framework is…
Modern power grids combine conventional generators with distributed energy resource (DER) generators in response to concerns over climate change and long-term energy security. Due to the intermittent nature of DERs, different types of…
We consider the problem of minimizing the difference in the demand and the supply of power using microgrids. We setup multiple microgrids, that provide electricity to a village. They have access to the batteries that can store renewable…
As more and more renewable intermittent generations are being connected to the distribution grid, the grid operators require more flexibility to maintain the balance between supply and demand. The intermittencies give rise to situations…
The growing penetration of distributed energy resources (DERs) is leading to continually changing operating conditions, which need to be managed efficiently by distribution grid operators. The intermittent nature of DERs such as solar…
The energy transition towards renewable based power provision requires improved monitoring and control of distributed energy resources (DERs), installed predominantly at the distribution grid level. Due to the gradual phase out of thermal…
In terms of the operation of microgrids, optimal scheduling is a vital issue that must be taken into account. In this regard, this paper proposes an effective framework for optimal scheduling of renewable microgrids considering energy…
In recent years, multi-access edge computing (MEC) is a key enabler for handling the massive expansion of Internet of Things (IoT) applications and services. However, energy consumption of a MEC network depends on volatile tasks that…
Demand Response (DR) has a widely recognized potential for improving grid stability and reliability while reducing customers energy bills. However, the conventional DR techniques come with several shortcomings, such as inability to handle…
Grid resilience is crucial in light of power interruptions caused by increasingly frequent extreme weather events. Well-designed energy management systems (EMS) have made progress in improving microgrid resilience through the coordination…
The evolution of smart microgrid and its demand-response characteristics not only will change the paradigms of the century-old electric grid but also will shape the electricity market. In this new market scenario, once always energy…
This work proposes an approach that integrates reinforcement learning and model predictive control (MPC) to solve finite-horizon optimal control problems in mixed-logical dynamical systems efficiently. Optimization-based control of such…
Microgrids offer a promising paradigm for integrating distributed energy resources, bolstering energy resilience, and reducing the impact of blackouts. However, their inherent decentralization and dynamic operation present substantial…
Purpose: The computation methods for modeling, controlling and optimizing the transforming grid are evolving rapidly. We review and systemize knowledge for a special class of computation methods that solve large-scale power grid…
Challenges and opportunities coexist in microgrids as a result of emerging large-scale distributed energy resources (DERs) and advanced control techniques. In this paper, a comprehensive review of microgrid control is presented with its…
We consider the problem of optimal reactive power compensation for the minimization of power distribution losses in a smart microgrid. We first propose an approximate model for the power distribution network, which allows us to cast the…
The growing renewable energy sources have posed significant challenges to traditional power scheduling. It is difficult for operators to obtain accurate day-ahead forecasts of renewable generation, thereby requiring the future scheduling…
The growing penetration of renewable generation in distribution networks, primarily deployed by end-use electricity customers, is changing the traditional load profile and inevitably makes supply-load balancing more challenging for grid…