António Couto

Impact of Weather Regimes on the Wind Power Ramp Forecast in Portugal

Short-term forecasting and diagnostic tools for severe changes of wind power production (power ramps) may provide reliable information for a secure power system operation at a small cost. Understanding the underlying role of the synoptic weather regimes (WRs) in triggering the wind power ramp events can be an added value to improve and complement the current forecast techniques. This work identifies and classifies the WRs over mainland Portugal associated with the occurrence of severe wind power ramps. The most representative WRs are identified on compressed surface level atmospheric data using principal component analysis by applying K-means clustering. The results show a strong association between some synoptic circulation patterns and step variations of the wind power production indicating the possibility to identify certain WRs that are prone to trigger severe wind power ramps, thus opening the possibility for future development of diagnostic warning systems for system operators use.

Multi-Agent Energy Markets with High Levels of Renewable Generation: A Case-Study on Forecast Uncertainty and Market Closing Time

This article uses an agent-based system to analyze the potential impacts of variable generation on wholesale electricity markets. Specifically, the article presents some important features of the agent-based system, introduces a method to forecast wind power, and describes a case study to analyze the impact of both wind forecast errors and high levels of wind generation on the outcomes of the day-ahead market. The case study involved two simulations: a base case, where the market closes at 12:00 noon and the bids of a wind producer agent are based on a forecast performed 12 to 36 hours ahead, and an updated forecast case, where the market closes at 6:00 p.m. and the bids of the wind producer agent are based on an updated forecast. The results indicate that wind power forecast uncertainty may indeed influence market prices, highlighting the importance of adaptations to the market closing time.

Agent-Based Simulation of Day-Ahead Energy Markets: Impact of Forecast Uncertainty and Market Closing Time on Energy Prices

This article uses an agent-based system to analyzethe potential impact of variable generation on wholesale electricity markets. In particular, it presents a case study to analyse the impact of both wind forecast errors and high levels of wind generation on the outcomes of the day-ahead market. The case study involves six representative days and three simulations (for each day): a base case, where the market closes at 12:00 noon and the bids of a wind producer agent are based on a forecast performed 12 to 36 hours ahead, an updated forecast case, where the market closes at 8:00 p.m., and a perfect case, where production data is offered. The simulation results indicate that wind power forecast uncertainty may influence market-clearing prices, highlighting the importance of potential adaptations to the day-ahead closing time.

Wind power producers in shorter gate closure markets and balancing markets

Wind power is increasingly integrated into power systems through electricity markets. To make the market operation economically possible for wind power producers, the period between bids and delivery (gate closure time) is crucial. Wind power will experience larger forecast errors the longer the gate closure time. This paper presents a case study for wind power producers in Portugal (MIBEL) and Finland and Denmark (Nordpool). Historical wind power forecasts and realized generation together with market prices are used as an example. First the day-ahead and balancing prices are analyzed to see impacts of existing wind power in the markets. Then analyses of imbalance costs are made to illustrate the benefits of shorter gate closure times for wind power producers. The option for wind power producers to become active agents also in imbalance markets is explored, by bidding part of the forecast error one hour before.

Wind power participation in electricity markets — The role of wind power forecasts

Wind power forecasting is an important tool to better integrate wind power into different electricity market frameworks. In the case of a Portuguese wind producer participating in the day-ahead market, the forecast is based on the initial and boundary conditions (IC) from the meteorological global models at 06:00 UTC in the day before the operation. Consequently, this constitutes a gap of 18 hours between the IC and the first delivery hour. Taking into account the availability of meteorological global forecast models data, this work investigates the influence of this gaps reduction and the resultant certainty gain effect of using the IC provided by the models at times nearer to the first delivery hour to the day-ahead electricity market. The certainty gain is estimated by using a probabilistic wind power forecast methodology coupled with a 2-stage stochastic optimization model in order to provide the bid volumes for the day-ahead market that enable maximum profit with an acceptable risk to a wind power producer. Expected imbalance values considering the actual IC (6:00 UTC) and proposed ICs (12:00 and 18:00 UTC) are compared. The results obtained demonstrate that the IC used for the wind power forecast strictly determines forecast accuracy for the day-ahead market. Therefore, market adaptations are recommended to deal with the wind power variability and uncertainty avoiding negotiations on the adjustment and reserve markets at higher costs.

Multi-agent Wholesale Electricity Markets with High Penetrations of Variable Generation: A Case-Study on Multivariate Forecast Bidding Strategies.

Electricity markets (EMs) are distributed in nature, complex and evolve continuously. The share of variable generation continue to grow and gain an increasing relevance in EMs. In most of the European countries, the day-ahead market closes at 12 UTC of the day before the day of operation. Consequently, wind power forecasts have to be made 18 to 42 h prior to production, what brings a signicant uncertainty to the process. This article uses an agent-based simulation tool, called MATREM (for Multi-Agent TRading in Electricity Markets), to analyze the potential benefits to the day-ahead market of changing the wind power forecast information, enabling the development of different bidding strategies. Results show that the effect of different bidding strategies on the levelized revenues is higher (when compared with the effect on the day-ahead market prices).

A Spatiotemporal Methodology for Deep Offshore Resource Assessment

Reliable wind measurement campaigns needed to address deep offshore wind energy deployment are constrained by their prohibitive installation and maintenance costs. Floating LIDAR systems are a viable alternative to bottom fixed met masts, although have survivability problems during storm seasons. This chapter presents a methodology, based on already well-established standards, able to reduce the offshore measurement campaigns duration by relating them with reliable and low-cost coastal measurements. A two-step calibration procedure, spatiotemporal, is presented to obtain an accurate characterization of the wind resource in deep offshore regions taking into account the time shift between the two measurement points associated with the different atmospheric phenomena propagation. The methodology is applied in two experimental case studies: the first one deals with measurements taken by a LIDAR installed on an islet (10 km away from the coast), while in the second case study a LIDAR system is mounted on a buoy in a deep offshore region. Results show that the added temporal calibration step is gradually more important as the distance between the measurements points increases. Precision enhancements on the order of 4–5 % were observed in the expected annual energy production for a given offshore site. The proposed calibration procedures presented can be applied in many phases of the offshore development: resource assessment, power performance evaluation and even for completion of missing data in a measurement campaign.