52,953 research outputs found
Review of trends and targets of complex systems for power system optimization
Optimization systems (OSs) allow operators of electrical power systems (PS) to optimally operate PSs and to also create optimal PS development plans. The inclusion of OSs in the PS is a big trend nowadays, and the demand for PS optimization tools and PS-OSs experts is growing. The aim of this review is to define the current dynamics and trends in PS optimization research and to present several papers that clearly and comprehensively describe PS OSs with characteristics corresponding to the identified current main trends in this research area. The current dynamics and trends of the research area were defined on the basis of the results of an analysis of the database of 255 PS-OS-presenting papers published from December 2015 to July 2019. Eleven main characteristics of the current PS OSs were identified. The results of the statistical analyses give four characteristics of PS OSs which are currently the most frequently presented in research papers: OSs for minimizing the price of electricity/OSs reducing PS operation costs, OSs for optimizing the operation of renewable energy sources, OSs for regulating the power consumption during the optimization process, and OSs for regulating the energy storage systems operation during the optimization process. Finally, individual identified characteristics of the current PS OSs are briefly described. In the analysis, all PS OSs presented in the observed time period were analyzed regardless of the part of the PS for which the operation was optimized by the PS OS, the voltage level of the optimized PS part, or the optimization goal of the PS OS.Web of Science135art. no. 107
A distributed accelerated gradient algorithm for distributed model predictive control of a hydro power valley
A distributed model predictive control (DMPC) approach based on distributed
optimization is applied to the power reference tracking problem of a hydro
power valley (HPV) system. The applied optimization algorithm is based on
accelerated gradient methods and achieves a convergence rate of O(1/k^2), where
k is the iteration number. Major challenges in the control of the HPV include a
nonlinear and large-scale model, nonsmoothness in the power-production
functions, and a globally coupled cost function that prevents distributed
schemes to be applied directly. We propose a linearization and approximation
approach that accommodates the proposed the DMPC framework and provides very
similar performance compared to a centralized solution in simulations. The
provided numerical studies also suggest that for the sparsely interconnected
system at hand, the distributed algorithm we propose is faster than a
centralized state-of-the-art solver such as CPLEX
Management and Control of Domestic Smart Grid Technology
Emerging new technologies like distributed generation, distributed storage, and demand-side load management will change the way we consume and produce energy. These techniques enable the possibility to reduce the greenhouse effect and improve grid stability by optimizing energy streams. By smartly applying future energy production, consumption, and storage techniques, a more energy-efficient electricity supply chain can be achieved. In this paper a three-step control methodology is proposed to manage the cooperation between these technologies, focused on domestic energy streams. In this approach, (global) objectives like peak shaving or forming a virtual power plant can be achieved without harming the comfort of residents. As shown in this work, using good predictions, in advance planning and real-time control of domestic appliances, a better matching of demand and supply can be achieved.\ud
\u
A Three-Step Methodology to Improve Domestic Energy Efficiency
Increasing energy prices and the greenhouse effect lead to more awareness of energy efficiency of electricity supply. During the last years, a lot of technologies have been developed to improve this efficiency. Next to large scale technologies such as windturbine parks, domestic technologies are developed. These domestic technologies can be divided in 1) Distributed Generation (DG), 2) Energy Storage and 3) Demand Side Load Management. Control algorithms optimizing a combination of these techniques can raise the energy reduction potential of the individual techniques. In this paper an overview of current research is given and a general concept is deducted. Based on this concept, a three-step optimization methodology is proposed using 1) offline local prediction, 2) offline global planning and 3) online local scheduling. The paper ends with results of simulations and field tests showing that the methodology is promising.\u
Emission-aware Energy Storage Scheduling for a Greener Grid
Reducing our reliance on carbon-intensive energy sources is vital for
reducing the carbon footprint of the electric grid. Although the grid is seeing
increasing deployments of clean, renewable sources of energy, a significant
portion of the grid demand is still met using traditional carbon-intensive
energy sources. In this paper, we study the problem of using energy storage
deployed in the grid to reduce the grid's carbon emissions. While energy
storage has previously been used for grid optimizations such as peak shaving
and smoothing intermittent sources, our insight is to use distributed storage
to enable utilities to reduce their reliance on their less efficient and most
carbon-intensive power plants and thereby reduce their overall emission
footprint. We formulate the problem of emission-aware scheduling of distributed
energy storage as an optimization problem, and use a robust optimization
approach that is well-suited for handling the uncertainty in load predictions,
especially in the presence of intermittent renewables such as solar and wind.
We evaluate our approach using a state of the art neural network load
forecasting technique and real load traces from a distribution grid with 1,341
homes. Our results show a reduction of >0.5 million kg in annual carbon
emissions -- equivalent to a drop of 23.3% in our electric grid emissions.Comment: 11 pages, 7 figure, This paper will appear in the Proceedings of the
ACM International Conference on Future Energy Systems (e-Energy 20) June
2020, Australi
- …