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Publicações

Publicações por CPES

2017

Predictive management of low-voltage grids

Autores
Reis, M; Garcia, A; Bessa, R; Seca, L; Gouveia, C; Moreira, J; Nunes, P; Matos, PG; Carvalho, F; Carvalho, P;

Publicação
CIRED - Open Access Proceedings Journal

Abstract

2017

Towards new data management platforms for a DSO as market enabler - UPGRID Portugal demo

Autores
Alonso, A; Couto, R; Pacheco, H; Bessa, R; Gouveia, C; Seca, L; Moreira, J; Nunes, P; Matos, PG; Oliveira, A;

Publicação
CIRED - Open Access Proceedings Journal

Abstract
In the framework of the Horizon 2020 project UPGRID, the Portuguese demo is focused on promoting the exchange of smart metering data between the DSO and different stakeholders, guaranteeing neutrality, efficiency and transparency. The platform described in this study, named the Market Hub Platform, has two main objectives: (i) to guarantee neutral data access to all market agents and (ii) to operate as a market hub for the home energy management systems flexibility, in terms of consumption shift under dynamic retailing tariffs and contracted power limitation requests in response to technical problems. The validation results are presented and discussed in terms of scalability, availability and reliability.

2017

Probabilistic Price Forecasting for Day-Ahead and Intraday Markets: Beyond the Statistical Model

Autores
Andrade, JR; Filipe, J; Reis, M; Bessa, RJ;

Publicação
SUSTAINABILITY

Abstract
Forecasting the hourly spot price of day-ahead and intraday markets is particularly challenging in electric power systems characterized by high installed capacity of renewable energy technologies. In particular, periods with low and high price levels are difficult to predict due to a limited number of representative cases in the historical dataset, which leads to forecast bias problems and wide forecast intervals. Moreover, these markets also require the inclusion of multiple explanatory variables, which increases the complexity of the model without guaranteeing a forecasting skill improvement. This paper explores information from daily futures contract trading and forecast of the daily average spot price to correct point and probabilistic forecasting bias. It also shows that an adequate choice of explanatory variables and use of simple models like linear quantile regression can lead to highly accurate spot price point and probabilistic forecasts. In terms of point forecast, the mean absolute error was 3.03 Euro/MWh for day-ahead market and a maximum value of 2.53 Euro/MWh was obtained for intraday session 6. The probabilistic forecast results show sharp forecast intervals and deviations from perfect calibration below 7% for all market sessions.

2017

Solar power forecasting with sparse vector autoregression structures

Autores
Cavalcante, L; Bessa, RJ;

Publicação
2017 IEEE MANCHESTER POWERTECH

Abstract
The strong growth that is felt at the level of photovoltaic (PV) power generation craves for more sophisticated and accurate forecasting methods that could be able to support its proper integration into the energy distribution network. Through the combination of the vector autoregression model (VAR) with the least absolute shrinkage and selection operator (LASSO) framework, a set of sparse VAR structures can be obtained in order to capture the dynamic of the underlying system. The robust and efficient alternating direction method of multipliers (ADMM), well known for its great ability dealing with high-dimensional data (scalability and fast convergence), is applied to fit the resulting LASSO-VAR variants. This spatial-temporal forecasting methodology has been tested, using 1-hour and 15-minutes resolution, for 44 microgeneration units time-series located in a city in Portugal. A comparison with the conventional autoregressive (AR) model is performed leading to an improvement up to 11%.

2017

Uncertainty Forecasting in a Nutshell

Autores
Dobschinski, J; Bessa, R; Du, PW; Geisler, K; Haupt, SE; Lange, M; Moehrlen, C; Nakafuji, D; de la Torre Rodriguez, MD;

Publicação
IEEE POWER & ENERGY MAGAZINE

Abstract
It is in the nature of chaotic atmospheric processes that weather forecasts will never be perfectly accurate. This natural fact poses challenges not only for private life, public safety, and traffic but also for electrical power systems with high shares of weather-dependent wind and solar power production. © 2012 IEEE.

2017

Improving Renewable Energy Forecasting With a Grid of Numerical Weather Predictions

Autores
Andrade, JR; Bessa, RJ;

Publicação
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY

Abstract
In the last two decades, renewable energy forecasting progressed toward the development of advanced physical and statistical algorithms aiming at improving point and probabilistic forecast skill. This paper describes a forecasting framework to explore information from a grid of numerical weather predictions (NWP) applied to both wind and solar energy. The methodology combines the gradient boosting trees algorithm with feature engineering techniques that extract the maximum information from the NWP grid. Compared to a model that only considers one NWP point for a specific location, the results show an average point forecast improvement (in terms of mean absolute error) of 16.09% and 12.85% for solar and wind power, respectively. The probabilistic forecast improvement, in terms of continuous ranked probabilistic score, was 13.11% and 12.06%, respectively.

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