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

Optimal Decentralized Protocols for Electric Vehicle Charging

We propose decentralized algorithms for optimally scheduling electric vehicle charging. The algorithms exploit the elasticity and controllability of electric vehicle related loads in order to fill the valleys in electric demand profile. We formulate a global optimization problem whose objective is to impose a generalized notion of valley-filling, study properties of the optimal charging profiles, and give decentralized offline and online algorithms to solve the problem. In each iteration of the proposed algorithms, electric vehicles choose their own charging profiles for the rest horizon according to the price profile broadcast by the utility, and the utility updates the price profile to guide their behavior. The offline algorithms are guaranteed to converge to optimal charging profiles irrespective of the specifications (e.g., maximum charging rate and deadline) of electric vehicles at the expense of a restrictive assumption that all electric vehicles are available for negotiation at the beginning of the planning horizon. The online algorithms relax this assumption by using a scalar prediction of future total charging demand at each time instance and yield near optimal charging profiles. The proposed algorithms need no coordination among the electric vehicles, hence their implementation requires low communication and computation capability. Simulation results are provided to support these results

Framework for Electric Vehicles and Photovoltaic Synergies

Historically road transport has been exclusively dominated by petrol and diesel engines. Both alternatives are proved to be unsustainable due to their environmental impacts and the limited nature of their primary resources. Today’s transportation sector in the European Union (EU) accounts for 23% of CO2 emissions, 72% of which is being emitted by road transport. The European Union’s CO2 emission regulation for new cars, has come as a response to set emission performance limits for new passenger cars with the goal of establishing a road map change for automotive sector. Furthermore, the EU has set challenging targets to reduce greenhouse gas emissions by 40% in 2030 (relative to emissions in 1990) and for energy consumed to be generated at least with 27% from renewable sources in 2030. As regards energy efficiency, the 2030 framework also indicated that the cost-effective delivery of the greenhouse gas emissions reduction target for 2030 would require increased energy savings of the order of 27%. The renewable energy directive particularly identified: technological innovation, energy efficiency and contribution of renewable energy sources in transport sector as one of the most effective tools in reaching the expected targets in terms of sustainability and security of the supply. In such context it is obvious that reaching these challenges will be certainly depending on the rollout of Electric Vehicles (EV) as a mean of sustainable transport, higher penetration of distributed renewable energy sources. One consequential challenge will consist in accommodating such paradigm in the most cost-efficient fashion through active involvement of customer and better flexibility of the demand. This report highlights the current trends and expected evolution in the EU in term of electromobility, Photovoltaic (PV) systems and smart grids, with the aim of identifying mutual synergies aiming at enabling: energy efficiency, sustainable transport and higher share of renewable energy sources in the final energy mix. A technical conceptual architecture for integration of EV facilities and distributed generation sources in the context of smart grid is proposed to identify the predictable penetration limits of PV systems and EV users.JRC.F.3-Energy Security, Systems and Marke

Open-Source, Open-Architecture SoftwarePlatform for Plug-InElectric Vehicle SmartCharging in California

This interdisciplinary eXtensible Building Operating System–Vehicles project focuses on controlling plug-in electric vehicle charging at residential and small commercial settings using a novel and flexible open-source, open-architecture charge communication and control platform. The platform provides smart charging functionalities and benefits to the utility, homes, and businesses.This project investigates four important areas of vehicle-grid integration research, integrating technical as well as social and behavioral dimensions: smart charging user needs assessment, advanced load control platform development and testing, smart charging impacts, benefits to the power grid, and smart charging ratepayer benefits