Enhanced Multi-Objective Energy Optimization by a Signaling Method

Soares, João; Borges, Nuno; Vale, Zita; Oliveira, Paulo Renato de

doi:10.3390/en9100807

Cited by 10 publications

(5 citation statements)

References 37 publications

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“…Moreover, it is yet not common to see works that incorporate vehicle-to-grid (V2G), DG, DR, and energy storage systems (ESS) simultaneously as in [45][46][47][48]. However, [47,48] do not include consideration of uncertainty of the energy resources.…”

Section: Complexitymentioning

confidence: 99%

Survey on Complex Optimization and Simulation for the New Power Systems Paradigm

et al. 2018

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This survey provides a comprehensive analysis on recent research related to optimization and simulation in the new paradigm of power systems, which embraces the so-called smart grid. We start by providing an overview of the recent research related to smart grid optimization. From the variety of challenges that arise in a smart grid context, we analyze with a significance importance the energy resource management problem since it is seen as one of the most complex and challenging in recent research. The survey also provides a discussion on the application of computational intelligence, with a strong emphasis on evolutionary computation techniques, to solve complex problems where traditional approaches usually fail. The last part of this survey is devoted to research on large-scale simulation towards applications in electricity markets and smart grids. The survey concludes that the study of the integration of distributed renewable generation, demand response, electric vehicles, or even aggregators in the electricity market is still very poor. Besides, adequate models and tools to address uncertainty in energy scheduling solutions are crucial to deal with new resources such as electric vehicles or renewable generation. Computational intelligence can provide a significant advantage over traditional tools to address these complex problems. In addition, supercomputers or parallelism opens a window to refine the application of these new techniques. However, such technologies and approaches still need to mature to be the preferred choice in the power systems field. In summary, this survey provides a full perspective on the evolution and complexity of power systems as well as advanced computational tools, such as computational intelligence and simulation, while motivating new research avenues to cover gaps that need to be addressed in the coming years.

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Section: Complexitymentioning

confidence: 99%

Survey on Complex Optimization and Simulation for the New Power Systems Paradigm

et al. 2018

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“…In References [22][23][24][25][26][27] different methods are proposed to implement a decentralized power management of microgrids using a voltage droop compensation technique or hierarchical control strategy. In particular: In Reference [26] the DC distribution is analyzed in terms of power quality, at this scope, a laboratory scale model of a DC microgrid is illustrated and analyzed in terms of supply quality to loads despite several fluctuations or faults; in Reference [27], instead, new DC microgrid system is proposed for the smart energy delivery, the control strategy is based on valuation of four major operation modes to intelligently distribute the power to the load module with a centralized control approach.…”

Section: A Brief Overview Of DC Microgrids Management and Controlmentioning

confidence: 99%

A Real-Life Application of a Smart User Network

et al. 2018

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Smart Community microgrids could help to improve overall energy efficiency reducing transmission and distribution losses and allowing the implementation of optimal load control and resource dispatching. In this context, the authors have proposed the realization of DC smart microgrids. They are considered as a future prospective according to the increase of DC loads and DC output type distribution energy sources such as Photovoltaic and energy storage systems. In this paper, a DC smart microgrid, called Smart User Network, realized in a real-life application as a part of pilot site under the national research project PON04_00146 Smart cities and Communities and Social innovation named “Reti, Edifici, Strade Nuovi Obiettivi Virtuosi per l’Ambiente e l’Energia” (RES NOVAE), is illustrated. The Smart User Network, is managed by a distributed and decentralized control logic, the DC Bus Signaling, which allows the converters to operate independently of each other according to a decentralized logic. It guarantees the reliability, the continuity and the quality of supply, optimizing the use of energy produced by renewable energy sources, also in stand-alone configuration. The most significant experimental results obtained both in grid-connected and stand-alone configuration are presented and discussed.

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“…Some constraints of this problem can be found in [8], such as EV charging and discharging rates, battery capacity and balance considering predicted demand and location, technical limits of ESSs, balance, and capacity in each period, dispatchable DG capacity and supplier's limits. In addition, an innovative DR model which considers a daily peak power pricing (…”

Section: A Mathematical Modelmentioning

confidence: 99%

Multi-objective robust optimization to solve energy scheduling in buildings under uncertainty

Soares

Vale

Borges

et al. 2017

2017 19th International Conference on Intelligent System Application to Power Systems (ISAP)

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With the high penetration of renewable generation in Smart Grids (SG), the uncertainty behavior associated with the forecast of weather conditions possesses a new degree of complexity in the Energy Resource Management (ERM) problem. In this paper, a Multi-Objective Particle Swarm Optimization (MOPSO) methodology is proposed to solve ERM problem in buildings with penetration of Distributed Generation (DG) and Electric Vehicles (EVs) and considering the uncertainty of photovoltaic (PV) generation. The proposed methodology aims to maximize profits while minimizing CO2 emissions. The uncertainty of PV generation is modeled with the use of Monte Carlo simulation in the evaluation process of the MOPSO core. Also, a robust optimization approach is adopted to select the best solution for the worst-case scenario of PV generation. A case study is presented using a real building facility from Brazil, to verify the effectiveness of the implemented robust MOPSO.

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Enhanced Multi-Objective Energy Optimization by a Signaling Method

Cited by 10 publications

References 37 publications

Survey on Complex Optimization and Simulation for the New Power Systems Paradigm

Survey on Complex Optimization and Simulation for the New Power Systems Paradigm

A Real-Life Application of a Smart User Network

Multi-objective robust optimization to solve energy scheduling in buildings under uncertainty

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