Sumario del Capítulo Tres 145

This section will explore load forecasting algorithms used in the control applications for minimising the energy consumption on LV grids with electric vehicles. Due to the electrification of transportation, electric vehicles are becoming key to achieving a sustainable transportation sector. Large-scale utilisation of the electric vehicles will affect the future power system by increasing the magnitude and uncertainty of LV electricity demand [101] [102] [103]. Therefore, electric vehicles charging demand scheduling and optimal modelling have been recently investigated in the literature.

Liu et al. [101] presented day ahead charging schemes for one-hour time resolution for 100% electric vehicles in a Nordic region electrical network. The objective function of charging schemes is to minimise charging costs and reduce peak demand. Five charging schemes have been developed based on the charging time and location. In all scenarios, Liu et al. assumed a specific average of charging demand (150 Wh/km) to calculate the energy consumption based on the assumption of knowing the detailed driving distance and charging availability in advance. In similar work related to electric vehicles charging demand forecast, Galván-López et al. [102] designed a demand side management system by setting an overnight charging schedule to maximise the charge of electric vehicles whilst balancing the load on the LV transformer side with the aim of reducing the electricity costs. However, the authors [102] assumed that all customers are willing to submit electric vehicle charging schedules in advance to calculate the electricity demand aggregator. Alonso et al. [103] used a Genetic Algorithm (GA) to develop a day-ahead optimal power charging schedule for electric vehicles. The aim of this schedule was to reduce peak demand in low voltage systems. In this study the electrical vehicle battery was assumed to be fully charged during a fixed time duration and it did not consider any flexibility and volatility in behaviour. Furthermore, the optimal scheduling in [103] assumed that the time of connection for charging and the initial and final state of charge were known in advance. The aforementioned research has only concentrated on developing day ahead electric vehicle charging demand and assumed that the charging schedules are

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deterministic. It has not focused on forecasting the demand or taking the uncertainty of charging demand forecasting into account. Utilisation of the electric vehicles will increase electricity demand and the uncertainty of demand forecast, therefore, an accurate electricity demand forecasting method for an LV network that considers the electric vehicle charging demand is required to simulate realistic power system performance. Amini et al. [71] developed an ARIMA model to generate hourly day-ahead electric load profiles including the charging demand of electric vehicle parking lots and conventional electrical loads. Firstly, the forecast model parameters were determined based on the optimum Root Mean Square Error (RMSE) over the historical load data as a decoupled forecaster. Then, to calculate the charging demand of electric vehicles for large scale integration, the historical daily driven distances data, and fuel consumption data were included. The charging demand model in [71] was developed based on a parking lot case to reduce the demand volatility nature. Finally, to predict the aggregate of the LV network demand [71], including the connected electric vehicles, the charging demand was estimated by finding the expected value of the distance driven and the charging duration. A probability density function was developed based on historical data and the charging demand model to find the estimated values of distance driven and charging duration [71]. The number of electric vehicles in each parking lot was fixed, which helps to generate an accurate forecast profile, especially with the daily driving patterns.

Arias and Bae [104], discussed the effect of traffic volume and weather conditions on the accuracy of a charging demand forecast. Their research focused on using real and historical data of traffic volume and weather conditions to determine traffic patterns and thus required charging demand by using a cluster analysis technique. In order to run the proposed forecast model from their study [104], the number of input variables needed to be determined. These input variables are month, day, and total number of electric vehicles. The model used this data to find the weather and day type data from the historical data and determine the charging starting times based on a clustering analysis and a gaussian distribution of the historical data. The model, then calculated the electric vehicles charging demand to generate an hourly day- ahead load profile. To calculate the charging demand, the authors assumed that the electric vehicles can be charged only once per day at home or in a work place based on assuming that the daily driven distance for a fully charged battery is more than the average daily travelled distance. Furthermore, they also assumed that the charging time is fixed and known in advance.

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The analysis of [104] shows how a forecast can play a significant role in understanding electric vehicle charging demand on small and large scales. Majidpour et al. [105] developed a k- Nearest Neighbour (kNN) forecast model to predict the hourly electric vehicle charging load a day in advance for a parking lot case study. The main objective of this research was to predict the electric vehicle energy consumption based on two different data sets: the electric vehicle charging record and parking station record. The paper’s analysis shows that there is a difference between the two data sets, where the charging record includes the electric vehicle user profile data for each charging event compared to the station record, which includes direct measurements for the charging points. To determine the charging demand, it was assumed that the electric vehicle was charged by 1 kWh in every hour of the charging mode. In [105], 18% of the outlet numbers for the kNN model’s results for charging station recorded data exceeded the 30% error, while the average was around 10% and the highest error was 48%. Finally, Poghosyan et al. [106] presented an agent-based model to forecast the aggregate half hourly electric vehicle demand for the LV substation level based on four future electric vehicle energy consumption scenarios that have different rates of low carbon technologies. The proposed forecast model and load demand data was updated on a yearly basis where the model targets long-term load forecast. This work [106] presented a forecast model for the increase in electric vehicle charging load over 10 years based on various scenarios, taking into account the rate of electric vehicle uptake.

To conclude, the literature reviewed shows the significance of developing an accurate load forecast to increase energy savings. As discussed, recent applications such as the LV network demand and EV are highly volatile and stochastic. This demand behaviour will increase the difficulties of creating an accurate forecast model. The parameters of each forecast model for EV demand in the literature was chosen based on specific research needs and availability of the data. The exogenous variables play a key role in improving the forecast model’s performance and different assumptions have been made to reduce the forecast error or fill the gap in the data available. In the previous literature [104] [105], the specific assumptions of each work helped the researchers to classify the data and forecast the demand. However, they do not show the impact of these assumptions on the forecast model accuracy. In addition, the exogenous variables such as the weather conditions or traffic information can minimise the errors in the model and help to improve the forecast performance.

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The literature studied in section 2.3.2 has introduced ARIMAX and ANN forecast models with exogenous variables as a very common forecast methods for LV level demands. The literature has shown the improvement in forecast performance which can be achieved when using appropriate exogenous variables compared to models which do not include these variables. Recently, forecast researchers have begun to investigate the benefits and impact of using different exogenous variables for LV application forecasts. The implantation of these algorithms, ARIMAX and ANN, to RTG crane network or port applications has not been considered in the literature to date and is one of the objectives of this thesis.