Santos, Sérgio F.
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Santos
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Sérgio F.
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Santos, Sérgio F.
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Sérgio F. Santos, doutorado em Engenharia Eletrotécnica e de Computadores, prof. auxiliar na Universidade Portucalense Infante D. Henrique e prof. Convidado na Universidade de Aveiro. Autor de mais de 30 publicações em jornais científicos e mais de 45 publicações em atas de conferencia, com um h-index de 20 e mais 2075 citações de acordo com SG, tendo supervisionadomais de 30 alunos entre mestrado e doutoramento, estágios curriculares e outros alunos com bolsas. Os interesses de investigação são “demand response”, “multi-energy systems”, “system flexibility”, “energy communities”, “virtual power plants” e “ancillary services” e “grid resilience”.
Afiliação:
REMIT – Research on Economics, Management and Information Technologies.
DCT - Departamento de Ciência e Tecnologia.
E41A-D6C6-E3D7
56483358000
GNM-6353-2022Research Projects
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REMIT – Research on Economics, Management and Information Technologies
Centro de investigação que que tem como objetivo principal produzir e disseminar conhecimento teórico e aplicado que possibilite uma maior compreensão das dinâmicas e tendências económicas, empresariais, territoriais e tecnológicas do mundo contemporâneo e dos seus efeitos socioeconómicos. O REMIT adota uma perspetiva multidisciplinar que integra vários domínios científicos: Economia e Gestão; Ciências e Tecnologia; Turismo, Património e Cultura.
Founded in 2017, REMIT – Research on Economics, Management and Information Technologies is a research unit of Portucalense University. Based on a multidisciplinary and interdisciplinary perspective it aims at responding to social challenges through a holistic approach involving a wide range of scientific fields such as Economics, Management, Science, Technology, Tourism, Heritage and Culture.
Grounded on the production of advanced scientific knowledge, REMIT has a special focus on its application to the resolution of real issues and challenges, having as strategic orientations:
- the understanding of local, national and international environment;
- the development of activities oriented to professional practice, namely in the business world.
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Publication Open Access Optimal stochastic conditional value at risk-based management of a demand response aggregator considering load uncertainty2021-11-03 - Vahid-Ghavidel, Morteza; Javadi, Mohammad Sadegh; Santos, Sérgio F.; Gough, Matthew; Shafie-khah, Miadreza; Catalão, João P. S.; Santos, Sérgio F.This paper models a novel demand response (DR) trading strategy. In this model, the DR aggregator obtains the DR from the end-users via two types of DR programs, i.e. a time-of-use (TOU) program and an incentive-based DR program. Then, it offers this DR to the wholesale market. Three consumer sectors, namely residential, commercial and industrial, are included in this problem. The DR program is dependent on their corresponding load profiles during the studied time horizon. This paper uses a mixed-integer linear programming (MILP) problem and it is solved using the CPLEX solver through a stochastic programming approach in GAMS. The risk measure chosen to represent the load uncertainty of the users who are participating in the DR program is Conditional Value-at-Risk (CVaR). The proposed problem is simulated and assessed through a case study of a test system. The results indicate that the industrial loads play a major role in the power system and this directly affects the DR program. Moreover, the risk-averse decision-maker in this model favors a reduced participation in the DR programs when compared to a decision-maker who is risk-neutral, since the risk-averse decision maker prefers to be more secure against uncertainties. In other words, an increase in risk factor results in a decrease in the participation rate of the consumers in DR programs.Publication Restricted Access Agent-based modeling of peer-to-peer energy trading in a smart grid environment2021-09 - Silva, Pedro; Gough, Matthew; Santos, Sérgio F.; Shafie-khah, M.; Catalão, João P. S.; Osório, Gerardo J.; Santos, Sérgio F.End users have become active participants in local electricity market transactions because of the growth of the smart grid concept and energy storage systems (ESS). This participation is optimized in this article using a stochastic two-stage model considering the day-ahead and real-time electricity market data. This model optimally schedules the operation of a Smart Home (SH) to meet its energy demand. In addition, the uncertainty of wind and photovoltaic (PV) generation is considered along with different appliances. In this paper, the participation of an EV (electric vehicle), together with the battery energy storage systems, which allow for the increase in bidirectional energy transactions are considered. Demand Response (DR) programs are also incorporated which consider market prices in real-time and impact the scheduling process. A comparative analysis of the performance of a smart home participating in the electricity market is carried out to determine an optimal DR schedule for the smart homeowner. The results show that the SH’s participation in a real-time pricing scheme not only reduces the operating costs but also leads to better than expected profits. Moreover, total, day-ahead and real-time expected profits are better in comparison with existing literature. The objective of this paper is to analyze the SH performance within the electrical market context so as to increase the system’s flexibility whilst optimizing DR schedules that can mitigate the variability of end-users generation and load demand.Publication Restricted Access Two-stage optimal operation of smart homes participating in competitive electricity markets2021-11-03 - Silva, Pedro; Gough, Matthew; Santos, Sérgio F.; Home-Ortiz, Juan M; Shafie-khah, Miadreza; Catalão, João P.S.; Osório, Gerardo J.; Santos, Sérgio F.End users have become active participants in local electricity market transactions because of the growth of the smart grid concept and energy storage systems (ESS). This participation is optimized in this article using a stochastic two-stage model considering the day-ahead and real-time electricity market data. This model optimally schedules the operation of a Smart Home (SH) to meet its energy demand. In addition, the uncertainty of wind and photovoltaic (PV) generation is considered along with different appliances. In this paper, the participation of an EV (electric vehicle), together with the battery energy storage systems, which allow for the increase in bidirectional energy transactions are considered. Demand Response (DR) programs are also incorporated which consider market prices in real-time and impact the scheduling process. A comparative analysis of the performance of a smart home participating in the electricity market is carried out to determine an optimal DR schedule for the smart homeowner. The results show that the SH’s participation in the real-time pricing, scheme not only reduces the operating costs but also leads to better than expected profits. Moreover, total, day-ahead, and real-time expected profits are better in comparison with existing literature. The objective of this paper is to analyze the SH performance within the electrical market context so as to increase the system’s flexibility whilst optimizing DR schedules that can mitigate the variability of end-users generation and load demand.Publication Open Access Hybrid IGDT-stochastic self-scheduling of a distributed energy resources aggregator in a multi-energy system2023-02-15 - Vahid-Ghavidel, Morteza; Shafie-khah, M.; Javadi, M.S.; Santos, Sérgio F.; Quijano, D.A.; Catalão, J.P.S.; Santos, Sérgio F.The optimal management of distributed energy resources (DERs) and renewable-based generation in multi-energy systems (MESs) is crucial as it is expected that these entities will be the backbone of future energy systems. To optimally manage these numerous and diverse entities, an aggregator is required. This paper proposes the self-scheduling of a DER aggregator through a hybrid Info-gap Decision Theory (IGDT)-stochastic approach in an MES. In this approach, there are several renewable energy resources such as wind and photovoltaic (PV) units as well as multiple DERs, including combined heat and power (CHP) units, and auxiliary boilers (ABs). The approach also considers an EV parking lot and thermal energy storage systems (TESs). Moreover, two demand response (DR) programs from both price-based and incentive-based categories are employed in the microgrid to provide flexibility for the participants. The uncertainty in the generation is addressed through stochastic programming. At the same time, the uncertainty posed by the energy market prices is managed through the application of the IGDT method. A major goal of this model is to choose the risk measure based on the nature and characteristics of the uncertain parameters in the MES. Additionally, the behavior of the risk-averse and risk-seeking decision-makers is also studied. In the first stage, the sole-stochastic results are presented and then, the hybrid stochastic-IGDT results for both risk-averse and risk-seeker decision-makers are discussed. The proposed problem is simulated on the modified IEEE 15-bus system to demonstrate the effectiveness and usefulness of the technique.