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International audience; Uncertainty in the upcoming production of photo-voltaic (PV) plants is a challenge for grid operations and also a source of revenue loss for PV plant operators participating in electricity markets, since they have to pay penalties for the mismatch between contracted and actual productions. Improving PV predictability is an area of intense research. In real-world applications, forecasts are often needed for different time frames (horizon, update frequency, etc.) and are derived by dedicated models for each time frame (i.e. for day ahead and for intra-day trading). This can result in both different forecasted values corresponding to the same horizon and discontinuities among time-frames. In this paper we address this problem by proposing a novel seamless probabilistic forecasting approach able to cover multiple time frames. It is based on the Analog Ensemble (AnEn) model, however it is adapted to consider the most appropriate input for each horizon from a pool of available input data. It is designed to be able to start at any time of day, for any forecast horizon, making it well-suited for applications like continuous trading. It is easy to maintain as it adapts to the latest data and does not need regular retraining. We enhance short-term predictability by considering data from satellite images and in situ measurements. The proposed model has low complexity compared to benchmark models and is trivially parallelizable. It achieves performance comparable to state-of-the-art models developed specifically for the short term (i.e. up to 6 hours) and the day ahead. The evaluation was carried out on a real-world case comprising three PV plants in France, over a period of one year.

International audience; The objective of this work is to reduce the storage dimensions required to operate a coupled photovoltaic (PV) and Battery Energy Storage System (BESS) in an electricity market, while keeping the same level of performance. Performance is measured either with the amount of errors between the energy sold on the market and the actual generation of the PV/BESS i.e. the imbalance, or directly with the revenue generated on the electricity market from the PV/BESS operation. Two solutions are proposed and tested to reduce the BESS size requirement. The first solution is to participate in electricity markets with an aggregation of several plants instead of a single plant, which effectively reduces the uncertainty of the PV power generation. The second is to participate in an intra-day market to reduce the BESS usage. To evaluate the effects of these two solutions on the BESS size requirement, we simulate the control of the PV/BESS system in an electricity market.

International audience; The valorization of photovoltaic (PV) energy generation involves several decision making processes at different times with different objectives. For example, a PV power plant coupled with a Battery Energy Storage System (BESS) has to provide bids in the day-ahead electricity market, but can also provide ancillary services. On the delivery day, it can also participate in intra-day trading sessions, and must decide which quantity to charge or discharge from the BESS in real-time. These successive decision-making processes all require forecasts of the energy production level for different forecast horizons.However, the models and the inputs used for the different forecast horizons are often different. A common result is that in situ measurements are more accurate for very-short term forecasts (real-time to one hour ahead forecasts), satellite data is better for short-term forecasts (up to 6 hours ahead), and Numerical Weather Predictions (NWP) are better for long-term forecasts (day-ahead and longer). Models also vary, with auto-regressive approaches being commonly used for very-short term forecasts, while longer forecast horizons use a wide range of machine learning models.The RES producers have thus to develop and maintain numerous forecasting models for the different decision-making processes they are involved in, usually fitted for each power plant. This increases further the complexity of the decision-making processes and can create problems regarding the continuity of the forecasts.In this work we propose a forecasting model for PV power generation that can use all the inputs mentioned before, and weights them according to the forecasting horizon. It can thus operate from very short-term to day-ahead forecast horizons with state-of-the-art performance. It can also directly provide probabilistic forecasts for an aggregation of power plants, thus allowing having a single forecasting model for managing a virtual power plant. The model follows the “lazy learning” paradigm, where generalization from the training set is only computed when a forecast is requested. Thus, the model is resilient to changes in the neighborhood of the plant (surrounding environment, partial outage, soiling, etc.)The model is based on the Analog Ensemble (AnEn) method. However it is structurally expanded to allow the method to use an arbitrary large number of inputs. Each input is then weighted depending on the forecast horizon. As an example, for a given input for one-hour ahead, the weight is computed based on the Mutual Information (MI) between the input and the PV power generation observed one hour later. This allows dynamically selecting the most relevant inputs depending on the horizon. The model is evaluated for short-term and day-ahead forecasts, and compared with a Quantile Regression Forest (QRF) for day-ahead forecasts, and a linear Auto-Regressive Integrated Moving Average (ARIMA) model for the short term forecasts. Results show that the AnEn model is competitive with the QRF model in day-ahead forecasting. It is also consistently better than the ARIMA model for short-term forecasting.

International audience; Short-term forecasts of generation or demand are required as inputs into several power system management functions. Forecast models are in general tuned to provide optimal accuracy and reliable estimations of associated uncertainty. In a second step, these forecasts are used as input in tools that perform various functions such as scheduling, reserves estimation, and trading in electricity markets. These functions often integrate algorithms that in turn optimize a value-related criterion, like cost, reliability, income, etc. The literature has shown that in some processes, like energy trading, this value may be optimized if a specific quantile forecast is selected rather than the forecast considered as most accurate. In this paper we propose a new data-driven approach in which the two steps of forecasting and decision-making are unified into one function that is optimized considering a single criterion, i.e. the value featured in the decision-making process. This approach allows us to bypass the use of specific forecasting models and could be extended to any decision-making process for which the inputs, outputs and objective functions are well defined. An intermediate approach is evaluated where a meta-optimization is applied to tune the forecast model as a function of the value it brings. This intermediate approach can also prove efficient, but does not allow us to bypass the forecasting models. The applications considered to evaluate the concept are photovoltaic and wind power generation trading in a day-ahead market, where we simultaneously optimize a forecasting model and a trading strategy, considering the final revenue on the electricity market as the objective function. For the meta-optimization, an Analog Ensemble (AnEn) model is used to forecast PV production, coupled with a Support Vector Regression (SVR) to forecast market prices. For the data-driven approach, we use an Extreme Learning Machine (ELM). As the objective function is highly complex and non-linear, the optimization is carried out by Particle Swarm Optimization (PSO).

Uncertainty in the upcoming production of photovoltaic (PV) plants is a challenge for grid operations and also a source of revenue loss for PV plant operators participating in electricity markets, since they have to pay penalties for the mismatch between contracted and actual productions. Improving PV predictability is an area of intense research. In real-world applications, forecasts are often needed for different time frames (horizon, update frequency, etc.) and are derived by dedicated models for each time frame (i.e. for day ahead and for intraday trading). This can result in both different forecasted values corresponding to the same horizon and discontinuities among time-frames. In this paper we address this problem by proposing a novel seamless probabilistic forecasting approach able to cover multiple time frames. It is based on the Analog Ensemble (AnEn) model, however it is adapted to consider the most appropriate input for each horizon from a pool of available input data. It is designed to be able to start at any time of day, for any forecast horizon, making it well-suited for applications like continuous trading. It is easy to maintain as it adapts to the latest data and does not need regular retraining. We enhance short-term predictability by considering data from satellite images and in situ measurements. The proposed model has low complexity compared to benchmark models and is trivially parallelizable. It achieves performance comparable to state-of-the-art models developed specifically for the short term (i.e. up to 6 hours) and the day ahead. The evaluation was carried out on a real-world case comprising three PV plants in France, over a period of one year.

International audience; The increasing share of photovoltaic (PV) power in the global energy mix presents a great challenge to power grid operators. In particular, PV powers intermittency caused by varying weather conditions can cause mismatches between energy production and expectation. Battery Energy Storage Systems (BESS) are often put forward as a good technological solution to these problems, as they are able to mitigate PV power forecast errors. However, the investment cost for such systems is high, which makes their use in operational contexts difficult. In this paper, we compare several strategies to manage a PV power plant coupled with a BESS in a market environment. They are obtained by stochastic optimization using a Model Predictive Control (MPC) approach. This paper proposes an approach that takes into account the ageing of the BESS, both at the day-ahead level and in the real-time control of the BESS, by modeling the cost associated with BESS usage. As a result, the BESS arbitrates between compensating forecast errors and preserving its own life expectancy, based on both PV production and price scenarios derived from probabilistic forecasts. A sensitivity analysis is also carried out to provide guidelines on the optimal sizing of the BESS capacity, depending on market characteristics and BESS prospective costs.