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Details

  • Name

    Cláudio Monteiro
  • Cluster

    Power and Energy
  • Role

    External Research Collaborator
  • Since

    01st January 1997
001
Publications

2019

Optimal Prosumer Scheduling in Transactive Energy Networks Based on Energy Value Signals

Authors
Lotfi, M; Monteiro, C; Javadi, MS; Shafie Khah, M; Catalao, JPS;

Publication
SEST 2019 - 2nd International Conference on Smart Energy Systems and Technologies

Abstract
We present a novel fully distributed strategy for joint scheduling of consumption and trading within transactive energy networks. The aim is maximizing social welfare, which itself is redefined and adapted for peer-to-peer prosumer-based markets. In the proposed scheme, hourly energy values are calculated to coordinate the joint scheduling of consumption and trading, taking into consideration both preferences and needs of all network participants. Electricity market prices are scaled locally based on hourly energy values of each prosumer. This creates a system where energy consumption and trading are coordinated based on the value of energy use throughout the day, rather than only the market price. For each prosumer, scheduling is done by allocating load (consumption) and supply (trading) blocks, maximizing the energy value globally and locally within the network. The proposed strategy was tested using a case study of typical residential prosumers. It was shown that the proposed model could provide potential benefits for both prosumers and the grid, albeit with a user-centered, fully distributed management model which relies solely on local scheduling in transactive energy networks. © 2019 IEEE.

2019

Energy performance of buildings with on-site energy generation and storage - An integrated assessment using dynamic simulation

Authors
Bot, K; Ramos, NMM; Almeida, RMSF; Pereira, PF; Monteiro, C;

Publication
JOURNAL OF BUILDING ENGINEERING

Abstract
The European Union aims to achieve a nearly zero energy balance in buildings by 2020. The present study takes into consideration the passive systems of the building, energy demand, and energy generated by the on-site photovoltaic and storage system, and how they interact in different scenarios. The study also considers the energy demand from the grid and the surplus of renewable energy. The software EnergyPlus was used and the parametric sensitivity simulation method was applied, taking into account blinds operation, ventilation strategies, HVAC operation schemes and battery storage capacity, in 96 scenarios. The results highlight that there is great variability between the considered scenarios, highlighting the importance of sizing methodologies for the passive systems and the use of optimized home management algorithms. It was found that the use of batteries with higher storage capacity increases the demand-supply from the on-site PV energy but decreases the amount of energy injected into the grid. The design of the PV and battery system based on yearly integrated simulations allows for an optimized solution. This study also emphasizes the importance of knowing the expected occupancy during the design phase, as a significant input to the sizing methodologies of the storage capacity and on-site generation.

2018

Analysis of spinning reserves in systems with variable power sources

Authors
Fonte, PM; Monteiro, C; Barbosa, FM;

Publication
International Conference on the European Energy Market, EEM

Abstract
In this paper is studied an approach based on risk assessment to solve the scheduling of a power production system with variable power sources. The spinning reserves resulting from the unit commitment are analyzed too. In this methodology there are no infeasible solutions, only more or less costly solutions associated to the operation risks, such as, load or renewable production curtailment. The uncertainty of forecasted production and load demand are defined by probability distribution functions. The methodology is tested in a real case study, an island with high penetration of renewable power production. Finally, forecasted and measured reserves are compared, once the reserves are strongly linked with the forecasting quality. The results of a real case study are presented and discussed. They show the difficulty to achieve complete robust solutions. © 2018 IEEE.

2018

Evolution of Demand Response: A Historical Analysis of Legislation and Research Trends

Authors
Lotfi, M; Monteiro, C; Shafie Khah, M; Catalao, JPS;

Publication
2018 20th International Middle East Power Systems Conference, MEPCON 2018 - Proceedings

Abstract
In the past two decades, interest in demand response (DR) schemes has grown exponentially. The need for DR has been driven by sustainability (environmental and socioeconomic) and cost-efficiency. The main premise of DR is to influence the timing and magnitude of consumption to match energy supply by sharing the benefits with consumers, ultimately aiming to optimize generation cost. As such, the first and primary enabler to DR was the establishment of contemporary electricity markets. Increased proliferation of Distributed Energy Resources (DER) and microgeneration further motivated the participation of consumers as active players in the market, popularizing DR and the wider category of Demand-Side Management (DSM) programs. Smart Grids (SG) have been an enabler to modern DR schemes, with smart metering data providing input to the underlying optimization and forecasting tools. The more recent emergence of the Internet of Energy (IoE), seen as the evolution of SG, is driven by increased Internet of Things (IoT)-enabling and high penetration of scalable and distributed energy resources. In this IoE paradigm being a fully decentralized network of energy prosumers, DR will continue to be a vital aspect of the grid in future Transactive Energy (TE) schemes, aiming for a more user-centered, energy-efficient, cost-saving, energy management approach. This paper investigates original motives and identifies the first mentions of DR in the legislative and scientific literature. Afterwards, the evolution of DR is tracked over the past four decades, attempting to study the co-influence of legislation and research by performing a thorough statistical analysis of research trends on the IEEE Xplore digital library. Finally, conclusions are made as to the current state of DR and future prospects of DR are discussed. © 2018 IEEE.

2018

New probabilistic price forecasting models: Application to the Iberian electricity market

Authors
Monteiro, C; Ramirez Rosado, IJ; Alfredo Fernandez Jimenez, LA; Ribeiro, M;

Publication
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS

Abstract
This article presents original Probabilistic Price Forecasting Models, for day-ahead hourly price forecasts in electricity markets, based on a Nadaraya-Watson Kernel Density Estimator approach. A Gaussian Kernel Density Estimator function is used for each input variable, which allows to calculate the parameters of the probability density function (PDF) of a Beta distribution for the hourly price variable. Thus, valuable information is obtained from PDFs such as point forecasts, variance values, quantiles, probabilities of prices, and time series representations of forecast uncertainty. A Reliability Indicator is also introduced to give a measure of "reliability" of forecasts. The Probabilistic Price Forecasting Models were satisfactorily applied to the real-world case study of the Iberian Electricity Market. Input variables of these models include recent prices, power demands and power generations in the previous day, power demands in the previous week, forecasts of demand, wind power generation and weather for the day-ahead, and chronological data. The best model, corresponding to the best combination of input variables that achieves the lowest MAE, obtains one of the highest Reliability Indicator values. A systematic analysis of MAE values of the Probabilistic Price Forecasting Models for different combinations of input variables showed that as more types of input variables were considered in these models, MAE values improved and Reliability Indicator values usually increased.