15,252 research outputs found
Smart Grid Technologies in Europe: An Overview
The old electricity network infrastructure has proven to be inadequate, with respect to modern challenges such as alternative energy sources, electricity demand and energy saving policies. Moreover, Information and Communication Technologies (ICT) seem to have reached an adequate level of reliability and flexibility in order to support a new concept of electricity networkāthe smart grid. In this work, we will analyse the state-of-the-art of smart grids, in their technical, management, security, and optimization aspects. We will also provide a brief overview of the regulatory aspects involved in the development of a smart grid, mainly from the viewpoint of the European Unio
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
Capturing Distribution Grid-Integrated Solar Variability and Uncertainty Using Microgrids
The variable nature of the solar generation and the inherent uncertainty in
solar generation forecasts are two challenging issues for utility grids,
especially as the distribution grid integrated solar generation proliferates.
This paper offers to utilize microgrids as local solutions for mitigating these
negative drawbacks and helping the utility grid in hosting a higher penetration
of solar generation. A microgrid optimal scheduling model based on robust
optimization is developed to capture solar generation variability and
uncertainty. Numerical simulations on a test feeder indicate the effectiveness
of the proposed model.Comment: IEEE Power and Energy Society General Meeting, 201
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Corrective receding horizon EV charge scheduling using short-term solar forecasting
Forecast errors can cause sub-optimal solutions in resource planning optimization, yet they are usually modeled simplistically by statistical models, causing unrealistic impacts on the optimal solutions. In this paper, realistic forecast errors are prescribed, and a corrective approach is proposed to mitigate the negative effects of day-ahead persistence forecast error by short-term forecasts from a state-of-the-art sky imager system. These forecasts preserve the spatiotemporal dependence structure of forecast errors avoiding statistical approximations. The performance of the proposed algorithm is tested on a receding horizon quadratic program developed for valley filling the midday net load depression through electric vehicle charging. Throughout one month of simulations the ability to flatten net load is assessed under practical forecast accuracy levels achievable from persistence, sky imager and perfect forecasts. Compared to using day-ahead persistence solar forecasts, the proposed corrective approach using sky imager forecasts delivers a 25% reduction in the standard deviation of the daily net load. It is demonstrated that correcting day-ahead forecasts in real time with more accurate short-term forecasts benefits the valley filling solution
Operational planning and bidding for district heating systems with uncertain renewable energy production
In countries with an extended use of district heating (DH), the integrated
operation of DH and power systems can increase the flexibility of the power
system achieving a higher integration of renewable energy sources (RES). DH
operators can not only provide flexibility to the power system by acting on the
electricity market, but also profit from the situation to lower the overall
system cost. However, the operational planning and bidding includes several
uncertain components at the time of planning: electricity prices as well as
heat and power production from RES. In this publication, we propose a planning
method that supports DH operators by scheduling the production and creating
bids for the day-ahead and balancing electricity markets. The method is based
on stochastic programming and extends bidding strategies for virtual power
plants to the DH application. The uncertain factors are considered explicitly
through scenario generation. We apply our solution approach to a real case
study in Denmark and perform an extensive analysis of the production and
trading behaviour of the DH system. The analysis provides insights on how DH
system can provide regulating power as well as the impact of uncertainties and
renewable sources on the planning. Furthermore, the case study shows the
benefit in terms of cost reductions from considering a portfolio of units and
both markets to adapt to RES production and market states
Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems
Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions
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