Hydro-wind optimal operation for joint bidding in day-ahead market: storage efficiency and impact of wind forecasting uncertainty

Wind power production is uncertain. The imbalance between committed and delivered energy in pool markets leads to the increase of system costs, which must be incurred by defaulting producers, thereby decreasing their revenues. To avoid this situation, wind producers can submit their bids together with hydro resources. Then the mismatches between the predicted and supplied wind power can be used by hydro producers, turbining or pumping such differences when convenient. This study formulates the problem of hydro-wind production optimization in operation contexts of pool market. The problem is solved for a simple three-reservoir cascade case to discuss optimization results. The results show a depreciation in optimal revenues from hydro power when wind forecasting is uncertain. The depreciation is caused by an asymmetry in optimal revenues from positive and negative wind power mismatches. The problem of neutralizing the effect of forecasting uncertainty is subsequently formulated and solved for the three-reservoir case. The results are discussed to conclude the impacts of uncertainty on joint bidding in pool market contexts.info:eu-repo/semantics/acceptedVersio

Spot price forecasting for best trading strategy decision support in the Iberian electricity market

The increasing volatility in electricity markets has reinforced the need for better trading strategies by both sellers and buyers to limit the exposure to losses. Accordingly, this paper proposes an electricity trading strategy based on a mid-term forecast of the average spot price and a risk premium analysis based on this forecast. This strategy can help traders (buyers and sellers) decide whether to trade in the futures market (of varying monthly maturity) or to wait and trade in the spot market. The forecast model consists of an Artificial Neural Network trained with the Long Short Term Memory architecture to predict the average monthly spot prices, using only market price-related data as input variables. Statistical analysis verified the correlation and dependency between variables. The forecast model was trained, validated and tested with price data from the Iberian Electricity Market (MIBEL), in particular the Spanish zone, between January 2015 and August 2019. The last year of this period was reserved for testing the performance of the proposed forecast model and trading strategy. For comparison purposes, the results of a forecasting model trained with the Extreme Learning Machine over the same period are also presented. In addition, the forecasted value of the average monthly spot price was used to perform a risk premium analysis. The results were promising, as they indicated benefits for traders adopting the proposed trading strategy, proving the potential of the forecast model and the risk premium analysis based on this forecast.info:eu-repo/semantics/publishedVersio

Multi-Objective Market Clearing Model with an Autonomous Demand Response Scheme

Demand response (DR) is known as a key solution in modern power systems and electricity markets for mitigating wind power uncertainties. However, effective incorporation of DR into power system operation scheduling needs knowledge of the price–elastic demand curve that relies on several factors such as estimation of a customer’s elasticity as well as their participation level in DR programs. To overcome this challenge, this paper proposes a novel autonomous DR scheme without prediction of the price–elastic demand curve so that the DR providers apply their selected load profiles ranked in the high priority to the independent system operator (ISO). The energy and reserve markets clearing procedures have been run by using a multi-objective decision-making framework. In fact, its objective function includes the operation cost and the customer’s disutility based on the final individual load profile for each DR provider. A two-stage stochastic model is implemented to solve this scheduling problem, which is a mixed-integer linear programming approach. The presented approach is tested on a modified IEEE 24-bus system. The performance of the proposed model is successfully evaluated from economic, technical and wind power integration aspects from the ISO viewpoint

Prospects of a Meshed Electrical Distribution System Featuring Large-Scale Variable Renewable Power

Electrical distribution system operators (DSOs) are facing an increasing number of challenges, largely as a result of the growing integration of distributed energy resources (DERs), such as photovoltaic (PV) and wind power. Amid global climate change and other energy-related concerns, the transformation of electrical distribution systems (EDSs) will most likely go ahead by modernizing distribution grids so that more DERs can be accommodated. Therefore, new operational strategies that aim to increase the flexibility of EDSs must be thought of and developed. This action is indispensable so that EDSs can seamlessly accommodate large amounts of intermittent renewable power. One plausible strategy that is worth considering is operating distribution systems in a meshed topology. The aim of this work is, therefore, related to the prospects of gradually adopting such a strategy. The analysis includes the additional level of flexibility that can be provided by operating distribution grids in a meshed manner, and the utilization level of variable renewable power. The distribution operational problem is formulated as a mixed integer linear programming approach in a stochastic framework. Numerical results reveal the multi-faceted benefits of operating distribution grids in a meshed manner. Such an operation scheme adds considerable flexibility to the system and leads to a more efficient utilization of variable renewable energy source (RES)-based distributed generation

Controller Design and Experimental Validation of A Solar Powered E-bike Charging Station

Electric Vehicles (EV) have gained interest over the past decade. Because of this, to support EV technology installation of charging stations are required. Charging EVs from renewable energy provides a sustainable means of transport. E-bikes can help mitigate some mobility problems, particularly in large cities and metropolitan areas. This paper shows the development and implementation of a solar e-bike charging station with photovoltaic production, with energy storage system. The implemented system has a centralized control and allow an efficient management of the various resources and contemplates the possibility of four simultaneous e- bikes where user identification is performed by RFID. Finally, it is provided a user interface through an HMI panel and a web page where it will be possible to access the DataLog to consult the user activity and all charging parameters. Keywords: Renewable energy, Solar charging station, Programmable logic controller &nbsp

A New Charging Algorithm for Li-Ion Battery Packs Based on Artificial Neural Networks

This paper shows the potential of artificial intelligence (AI) in Li-ion battery charging methods by introducing a new charging algorithm based on artificial neural networks (ANNs). The proposed charging algorithm is able to find an optimized charging current profile, through ANNs, considering the real-time conditions of the Li-ion batteries. To test and validate the proposed approach, a low-cost battery management system (BMS) was developed, supporting up to 168 cells in series and n cells in parallel. When compared with the multistage charging algorithm, the proposed charging algorithm revealed a shorter charging time (7.85%) and a smaller temperature increase (32.95%). Thus, the results show that the proposed algorithm based on AI is able to effectively charge and balance batteries and can be regarded as a subject of interest for future research

A New Charging Algorithm for Li-Ion Battery Packs Based on Artificial Neural Networks

This paper shows the potential of artificial intelligence (AI) in Li-ion battery charging methods by introducing a new charging algorithm based on artificial neural networks (ANNs). The proposed charging algorithm is able to find an optimized charging current profile, through ANNs, considering the real-time conditions of the Li-ion batteries. To test and validate the proposed approach, a low-cost battery management system (BMS) was developed, supporting up to 168 cells in series and n cells in parallel. When compared with the multistage charging algorithm, the proposed charging algorithm revealed a shorter charging time (7.85%) and a smaller temperature increase (32.95%). Thus, the results show that the proposed algorithm based on AI is able to effectively charge and balance batteries and can be regarded as a subject of interest for future research