A Novel Mean Field Game-Based Strategy for Charging Electric Vehicles in Solar Powered Parking Lots

We develop a strategy, with concepts from Mean Field Games (MFG), to coordinate the charging of a large population of battery electric vehicles (BEVs) in a parking lot powered by solar energy and managed by an aggregator. A yearly parking fee is charged for each BEV irrespective of the amount of energy extracted. The goal is to share the energy available so as to minimize the standard deviation (STD) of the state of charge (SOC) of batteries when the BEVs are leaving the parking lot, while maintaining some fairness and decentralization criteria. The MFG charging laws correspond to the Nash equilibrium induced by quadratic cost functions based on an inverse Nash equilibrium concept and designed to favor the batteries with the lower SOCs upon arrival. While the MFG charging laws are strictly decentralized, they guarantee that a mean of instantaneous charging powers to the BEVs follows a trajectory based on the solar energy forecast for the day. That day ahead forecast is broadcasted to the BEVs which then gauge the necessary SOC upon leaving their home. We illustrate the advantages of the MFG strategy for the case of a typical sunny day and a typical cloudy day when compared to more straightforward strategies: first come first full/serve and equal sharing. The behavior of the charging strategies is contrasted under conditions of random arrivals and random departures of the BEVs in the parking lot.

PSO Based Optimized Ensemble Learning and Feature Selection Approach for Efficient Energy Forecast

Swarm intelligence techniques with incredible success rates are broadly used for various irregular and interdisciplinary topics. However, their impact on ensemble models is considerably unexplored. This study proposes an optimized-ensemble model integrated for smart home energy consumption management based on ensemble learning and particle swarm optimization (PSO). The proposed model exploits PSO in two distinct ways; first, PSO-based feature selection is performed to select the essential features from the raw dataset. Secondly, with larger datasets and comprehensive range problems, it can become a cumbersome task to tune hyper-parameters in a trial-and-error manner manually. Therefore, PSO was used as an optimization technique to fine-tune hyper-parameters of the selected ensemble model. A hybrid ensemble model is built by using combinations of five different baseline models. Hyper-parameters of each combination model were optimized using PSO followed by training on different random samples. We compared our proposed model with our previously proposed ANN-PSO model and a few other state-of-the-art models. The results show that optimized-ensemble learning models outperform individual models and the ANN-PSO model by minimizing RMSE to 6.05 from 9.63 and increasing the prediction accuracy by 95.6%. Moreover, our results show that random sampling can help improve prediction results compared to the ANN-PSO model from 92.3% to around 96%.

Photovoltaic Power Forecasting Methods

The rapid growth in grid penetration of photovoltaic (PV) calls for more accurate methods to forecast the performance and reliability of PV. Several methods have been proposed to forecast the PV power generation at different temporal horizons. In this chapter the different methods used in PV power forecasting are described with an example on their applications and related uncertainty. The methods discussed include physical, heuristic, statistical and machine learning methods. When benchmarked, it is shown that physical method showed the highest uncertainties compared to other methods. In the chapter, the effect of degradation on lifetime PV power and energy forecast is also assessed using linear and non-linear degradation scenarios. It is shown that the relative difference in lifetime yield prediction is over 5% between linear and non-linear scenarios.

Wind Energy Forecast Conditioned on Großwetterlage (large scale weather situation)

<p>Obtaining a quantitative measure for the uncertainty of forecasts for renewable energy has proven to be a challenging problem in the past. We present results on predicting uncertainty of a forecast conditioned on the large weather situation (Großwetterlage). As a first attempt, we use the objective weather classification by the German Meteorological Service (DWD), which sorts the weather into 40 situations based on wind direction, cyclonality and moisture in the atmosphere.</p><p>The considered forecasts concern the day-ahead production of solar power for two exemplary solar parks. To quantify the uncertainty, we define five different metrics (based on normalized absolute error and probability distribution), where each one is trained individually using machine learning. As a result, we obtain measures for over- and underprediction conditioned on the said Großwetterlage.</p><p>We consider this to be a very promising yet accessible approach to derive a quantitative measure for uncertainties based on the current, day-to-day weather situation. Future work may concern an improvement of the Großwetterlagencharacterization and a general, probabilistic formulation of the problem, e.g. using Bayesian inference.</p>