Browsing by Author "Borges, Nuno"
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- Anti-Angiogenic Properties of Cafestol and Kahweol Palmitate Diterpene EstersPublication . Moeenfard, Marzieh; Cortez, Alice; Machado, Vera; Costa, Raquel; Luís, Carla; Coelho, Pedro; Soares, Raquel; Alves, Arminda; Borges, Nuno; Santos, AlejandroEpidemiological studies support the association of coffee-specific diterpenes, with various beneficial health effects. Although anti-antiangiogenic properties of free cafestol and kahweol have been recently described, available data regarding their esterified form, in particular palmitate esters as the main diterpene esters present in coffee, are still rare. Given that angiogenesis plays an important role in many pathological conditions, including cancer growth and metastasis, this study aimed to assess and compare the potential anti-angiogenic effects of cafestol palmitate (CP) and kahweol palmitate (KP) in an in vitro angiogenesis model. According to our findings, both compounds inhibited angiogenesis steps on human microvascular endothelial cells (HMVECs), although a more significant effect was observed for KP. Compared to control, HMVECs viability decreased in a dose-dependent manner upon incubation either with CP or KP. Concentrations of 75 and 100 μM of each compound were cytotoxic. Cell proliferation was also dramatically reduced by both diterpene esters at 50 μM, although KP had a stronger inhibitory effect. However, CP and KP did not induce apoptosis on HMVECs. Both compounds reduced cell migration, but this effect was only statistically significant after KP incubation. Inhibition of VEGFR2 expression and its downstream effector Akt, but not Erk, was also observed in CP- and KP-treated HMVECs. These findings were confirmed using ELISA assay for phosphorylated (active) VEGFR-2. Taken together, these data indicate that both CP and KP can be considered potent compounds against angiogenesis-dependent disorders. Our findings further indicate that KP exerts more potent anti-angiogenic effects than CP, in most of assays.
- Current status and new business models for electric vehicles demand response design in smart gridsPublication . Soares, João; Vale, Zita; Borges, NunoGlobal electric vehicles sales increased about 10 times from 2011, reaching more than 1 million vehicles in roads by 2015. This number is very likely to increase at a steady pace as more models are made available and battery technology improves and costs decrease. It is recognized that the electric vehicles mass integration will imply more complexity to the operation and planning tasks of power systems, but also allow additional opportunities. Indeed, demand response can play a major role to integrate electric vehicles in the future smart grid. This paper discusses the current initiatives from the retailing business in Portugal, Spain and Germany to deal with electric vehicles integration and discusses some new demand response models shaped for the smart grid that can be the new business model of tomorrow energy providers. Currently, the electric vehicles demand response measures adopted by the industry are very limited, mostly offering time of use tariffs with a discount rate.
- D7.2 Proceedings of the First DREAM-GO Workshop: Simulation of consumers and markets towards real time demand responsePublication . Vale, Zita; Borges, Nuno; Faria, Pedro; Soares, João; Villarrubia, Gabriel; Corchado, Juan M.; Boldt, Diogo; Venayagamoorthy, G. Kumar; Matos, Luisa; Landeck, Jorge; Ferreira, Rodrigo; Spinola, João; Barriuso, Alberto L.; Paz, Juan F. deProceedings of the First DREAM-GO Workshop Institute of Engineering - Polytechnic of Porto, Porto, Portugal, April 6-7, 2016. Simulation of consumers and markets towards real time demand response
- Dynamic electricity pricing for electric vehicles using stochastic programmingPublication . Soares, João; Ghazvini, Mohammad Ali Fotouhi; Borges, Nuno; Vale, ZitaElectric Vehicles (EVs) are an important source of uncertainty, due to their variable demand, departure time and location. In smart grids, the electricity demand can be controlled via Demand Response (DR) programs. Smart charging and vehicle-to-grid seem highly promising methods for EVs control. However, high capital costs remain a barrier to implementation. Meanwhile, incentive and price-based schemes that do not require high level of control can be implemented to influence the EVs’ demand. Having effective tools to deal with the increasing level of uncertainty is increasingly important for players, such as energy aggregators. This paper formulates a stochastic model for day-ahead energy resource scheduling, integrated with the dynamic electricity pricing for EVs, to address the challenges brought by the demand and renewable sources uncertainty. The two-stage stochastic programming approach is used to obtain the optimal electricity pricing for EVs. A realistic case study projected for 2030 is presented based on Zaragoza network. The results demonstrate that it is more effective than the deterministic model and that the optimal pricing is preferable. This study indicates that adequate DR schemes like the proposed one are promising to increase the customers’ satisfaction in addition to improve the profitability of the energy aggregation business.
- Dynamic electricity pricing for electric vehicles using stochastic programmingPublication . Soares, João; Ghazvini, Mohammad Ali Fotouhi; Borges, Nuno; Vale, ZitaElectric Vehicles (EVs) are an important source of uncertainty, due to their variable demand, departure time and location. In smart grids, the electricity demand can be controlled via Demand Response (DR) programs. Smart charging and vehicle-to-grid seem highly promising methods for EVs control. However, high capital costs remain a barrier to implementation. Meanwhile, incentive and price-based schemes that do not require high level of control can be implemented to influence the EVs' demand. Having effective tools to deal with the increasing level of uncertainty is increasingly important for players, such as energy aggregators. This paper formulates a stochastic model for day-ahead energy resource scheduling, integrated with the dynamic electricity pricing for EVs, to address the challenges brought by the demand and renewable sources uncertainty. The two-stage stochastic programming approach is used to obtain the optimal electricity pricing for EVs. A realistic case study projected for 2030 is presented based on Zaragoza network. The results demonstrate that it is more effective than the deterministic model and that the optimal pricing is preferable. This study indicates that adequate DR schemes like the proposed one are promising to increase the customers' satisfaction in addition to improve the profitability of the energy aggregation business.
- Enhanced Multi-Objective Energy Optimization by a Signaling MethodPublication . Soares, João; Borges, Nuno; Vale, Zita; Oliveira, P.B. de MouraIn this paper three metaheuristics are used to solve a smart grid multi-objective energy management problem with conflictive design: how to maximize profits and minimize carbon dioxide (CO2) emissions, and the results compared. The metaheuristics implemented are: weighted particle swarm optimization (W-PSO), multi-objective particle swarm optimization (MOPSO) and non-dominated sorting genetic algorithm II (NSGA-II). The performance of these methods with the use of multi-dimensional signaling is also compared with this technique, which has previously been shown to boost metaheuristics performance for single-objective problems. Hence, multi-dimensional signaling is adapted and implemented here for the proposed multi-objective problem. In addition, parallel computing is used to mitigate the methods’ computational execution time. To validate the proposed techniques, a realistic case study for a chosen area of the northern region of Portugal is considered, namely part of Vila Real distribution grid (233-bus). It is assumed that this grid is managed by an energy aggregator entity, with reasonable amount of electric vehicles (EVs), several distributed generation (DG), customers with demand response (DR) contracts and energy storage systems (ESS). The considered case study characteristics took into account several reported research works with projections for 2020 and 2050. The findings strongly suggest that the signaling method clearly improves the results and the Pareto front region quality.
- European Policies Aiming the Penetration of Distributed Energy Resources in the Energy MarketPublication . Borges, Nuno; Spínola, João; Boldt, Diogo; Faria, Pedro; Vale, ZitaEnergy policies have been widely developed in the recent past as sequence of the increasing relevance of distributed energy resources potential in power systems, namely in achieving the reduction of carbon dioxide emissions gaining independence from fossil fuels. Thus, the main factions in the world, as North America and Europe, have been focusing on the implementation of new energy policies capable of managing several types of energy sources considering their decentralized characteristics. In this way, the present work provides an introduction of how the new energy policies, concerning distributed energy resources, are working towards the increase of these resources penetration in the energy mix. Some successful case studies are presented, namely from Europe, to assess the benefits of such policies to consumers, to producers and to energy market as a whole.
- Evaluation of Smart Grid Implementations in the Consumer's Energy BillPublication . Boldt, Diogo; Borges, Nuno; Spínola, João; Faria, Pedro; Vale, ZitaThe need to increase the share of renewable energy resources in several countries around the world led to the development of new strategies, in order to implement more effective energy policies. These resources have a distributed nature and are one of the main paths to incentive the reduction of CO2 emissions, and their impact on the environment. Many countries are making efforts to advance in the implementation of their own strategies to achieve the fossil fuels independency. Therefore, the intent is to stipulate energy policies that increase renewable energy share in the energy mix. These policies depend on regulations, taxation, incentives and promotional schemes. In this paper, it is presented a brief introduction to the energy policies in two countries, Denmark and Finland. Demand Response (DR) aspects and its impact in the energy market are also discussed.
- Multi-objective Particle Swarm Optimization to Solve Energy Scheduling with Vehicle-to-Grid in Office Buildings Considering UncertaintiesPublication . Borges, Nuno; Soares, João; Vale, ZitaThis paper presents a Multi-Objective Particle Swarm Optimization (MOPSO) methodology to solve the problem of energy resource management in buildings with a penetration of Distributed Generation (DG) and Electric Vehicles (EVs). The proposed methodology consists in a multi-objective function, in which it is intended to maximize the profit and minimize CO2 emissions. This methodology considers the uncertainties associated with the production of electricity by the photovoltaic and wind energy sources. This uncertainty is modeled with the use of a robust optimization approach in the metaheuristic. A case study is presented using a real building facility from Portugal, in order to verify the feasibility of the implemented robust MOPSO.
- Multi-objective robust optimization to solve energy scheduling in buildings under uncertaintyPublication . Soares, João; Vale, Zita; Borges, Nuno; Lezama, Fernando; Kagan, NelsonWith 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 CO 2 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.