Challenges in integrating distributed Energy storage systems into future smart grid

Mohd, A.; Ortjohann, E.; Schmelter, A.; Hamsic, N.; Morton, D.

doi:10.1109/isie.2008.4676896

Cited by 230 publications

(147 citation statements)

References 3 publications

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“…In recent years, these solution concepts started to pose new challenges to the existing power grids, whose hierarchical, centrally-controlled structure has remained unchanged for a century. For example, the exploitation of renewable sources such as solar or wind may be problematic due to their variable and intermittent nature [38], while the integration of distributed energy resources may cause congestion and atypical power flows that threaten system's reliability [27]. On the other hand, as a part of smart grid initiatives, smart meters have been widely deployed to understand the energy consumption behavior of the demand side.…”

Section: Introductionmentioning

confidence: 99%

Cluster-based aggregate forecasting for residential electricity demand using smart meter data

Wijaya

Vasirani

Humeau

et al. 2015

2015 IEEE International Conference on Big Data (Big Data)

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Abstract-While electricity demand forecasting literature has focused on large, industrial, and national demand, this paper focuses on short-term (1 and 24 hour ahead) electricity demand forecasting for residential customers at the individual and aggregate level. Since electricity consumption behavior may vary between households, we first build a feature universe, and then apply Correlation-based Feature Selection to select features relevant to each household. Additionally, smart meter data can be used to obtain aggregate forecasts with higher accuracy using the so-called Cluster-based Aggregate Forecasting (CBAF) strategy, i.e., by first clustering the households, forecasting the clusters' energy consumption separately, and finally aggregating the forecasts. We found that the improvement provided by CBAF depends not only on the number of clusters, but also more importantly on the size of the customer base.

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Section: Introductionmentioning

confidence: 99%

Cluster-based aggregate forecasting for residential electricity demand using smart meter data

Wijaya

Vasirani

Humeau

et al. 2015

2015 IEEE International Conference on Big Data (Big Data)

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“…The challenges of integrating renewable energy such as power generated from wind turbines, solar cells, and other renewable sources are handling the variability of these sources due to their intermittent and various nature [5,29]. Solar cells produce energy when they have direct sunlight, wind turbines generate power when there is wind.…”

Section: Role Of Batteries In the Power Grid And Micro-gridsmentioning

confidence: 99%

Integration of alternative energy sources into digital micro‐grids

Rojas‐Cessa

Grebel

Jiang

et al. 2017

Env Prog and Sustain Energy

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We overview different alternative energy resources and comment on their benefits and challenges. We highlight some features of alternative and sustainable energy resources and discuss some challenges for their integration into the power grid. In view of global trend toward blending alternatives and conventional energy resources in electrical power grids, a more robust energy distribution approach is necessary. Here, we present an alternative to the power grid with great potential for integrating alternative energy sources and defining microgrids-the Controlled-Delivery Grid (CDG). We comment how this new digital approach enhances the distribution of energy may help to resolve those challenges. We also examine the role of energy storage elements (batteries, super-capacitors), and sustainable energy resources in this new approach to the power grid. We provide some numerical analysis on the performance of energy distribution of the CDG and some experimental results that highlight some of its features. V C 2017 American Institute of Chemical Engineers Environ Prog, 37: 155-164, 2018

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“…If the load analysis or evaluation for buildings is not detailed enough, it will exert a great deal of influence on operations [42,43]. However, energy supply and demand are usually characterized by strong uncertainty [44].…”

Section: Introductionmentioning

confidence: 99%

An MILP Method for Design of Distributed Energy Resource System Considering Stochastic Energy Supply and Demand

Duan

Yan

et al. 2017

Energies

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A distributed energy resource (DER) system, which can be defined as a medium or small energy conversion and utilization system with various functions for meeting multiple targets, is directly oriented towards users and achieves on-site production and energy supply according to users' demands. Optimization research on system construction has recently become an important issue. In this paper, simple stochastic mathematical equations were used to interpret the optimal design problem of a DER system, and based on this, a novel method for solving the optimization problem, which has multi-dimensional stochastic uncertainties (involving the price of input-energy and energy supply and demand), was put forward. A mixed-integer linear programming (MILP) model was established for the optimal design of the DER system by combining the ideas of mean value and variance, aiming to minimize the total costs, including facility costs, energy purchase costs, and loss caused by energy supply shortage, and considering the energy balance and facility performance constraints. In the end, a DER system design for an office building district in Xuzhou, China, was taken as an example to verify the model. The influences of uncertainty on the selection of system facilities and the economic evaluation were analyzed. The result indicated that uncertainty of energy demand played a significant role in optimal design, whereas energy price played a negligible role. With respect to economy, if uncertainties are not considered in system design, it will result in a short supply, and therefore the total cost will increase considerably. The calculation convergence was compared with previous work. The implementation results showed the practicality and efficiency of the proposed method.

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Challenges in integrating distributed Energy storage systems into future smart grid

Cited by 230 publications

References 3 publications

Cluster-based aggregate forecasting for residential electricity demand using smart meter data

Cluster-based aggregate forecasting for residential electricity demand using smart meter data

Integration of alternative energy sources into digital micro‐grids

An MILP Method for Design of Distributed Energy Resource System Considering Stochastic Energy Supply and Demand

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