Short-term uncertainty in the dispatch of energy resources for VPP: A novel rolling horizon model based on stochastic programming

The intermittent nature of distributed energy resources introduces new degrees of uncertainty in the operation of energy systems; hence, short-term decisions can no longer be considered fully deterministic. In this article, an energy management system (EMS) was proposed to optimize the market participation and the real-time operation of a virtual power plant (VPP) composed of photovoltaic generators, non-flexible loads, and storage systems (e-vehicle, stationary battery, and thermal storage). The market bidding process was optimized through a two-stage stochastic formulation, which considered the day-ahead forecast uncertainty to minimize the energy cost and make available reserve margins in the ancillary service market. The real-time management of regulating resources was obtained through an innovative rolling horizon stochastic programming model, taking into account the effects of short-term uncertainties. Numerical simulations were carried out to demonstrate the effectiveness of the proposed EMS. The architecture proved to be effective in managing several distributed resources, enabling the provision of ancillary services to the power system. In particular, the model developed allowed an increase in the VPP's profits of up to 11% and a reduction in the energy imbalance of 25.1% compared to a deterministic optimization

Development and modelling of different decarbonization scenarios of the European energy system until 2050 as a contribution to achieving the ambitious 1.5 ∘C climate target—establishment of open source/data modelling in the European H2020 project openENTRANCE

The ambition of the openENTRANCE project is to develop and establish an open, transparent and integrated modelling platform for assessing low-carbon transition pathways of the European energy system. In this context, the open source energy system model GENeSYS-MOD is one of the core models having been developed enabling quantitative scenario pathway studies of the future European energy system. The four quantitative studies presented in the openENTRANCE project and in this paper build upon the four storylines developed at the beginning of the openENTRANCE project. A storyline is a narrative describing possible future trajectories (pathways) of the energy transition. Storylines should be understood as possible future developments of the European energy system, which could occur equally without having a preference for one of them. Three of the storylines, and subsequently quantified scenario pathway studies in openENTRANCE comply with the (European fraction of the) 1.5 ∘C global temperature increase limit. The fourth one approaches the 2.0 ∘C limit. The quantified scenario pathway results not only show the needs of the fully open energy system model GENeSYS-MOD to find feasible solutions of the underlying analytical optimization problem, but more importantly highlight what needs to be done in the future European energy system if we seriously intend to limit global warming

Optimal Singular Value Decomposition Based Big Data Compression Approach in Smart Grids

The smart grid is a fully automatic delivery grid for electricity power with a two-way reliable flow of electricity and information among different equipment on the grid. Smart meters and sensors monitoring the system provide a huge amount of data in various part of smart grid. To logically manage this trouble, a new lossy data compression approach for big data compression is proposed. The optimal singular value decomposition (SVD) is applied to a matrix that achieves the optimal number of singular values to the sending process, and the other ones will be neglected. This goal is done due to the quality of retrieved data and the compression ratio. In the presented scheme, to implement the optimization framework, various intelligent optimization methods are used to determine the number of optimal values in the elimination stage. The efficiency and capabilities of the proposed method are examined using a wide range of data types, from electricity market data to image processing benchmarks. The comparisons show that the compression level obtained by the proposed method can dominate the points given by the existing SVD rank reduction methods. Also, as the other finding of this article, the performance of the rank reduction methods depends on the application and data types. It means that a rank reduction method can reveal a good performance in one application and performs unacceptably for another purpose. So, the optimized rank reduction can pave the way toward a robust and reliable performance.©2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed