Optimising Energy Flexibility of Boats in PV-BESS Based Marina Energy Systems

Implementation of alternative energy supply solutions requires the broad involvement of local communities. Hence, smart energy solutions are primarily investigated on a local scale, resulting in integrated community energy systems (ICESs). Within this framework, the distributed generation can be optimally utilised, matching it with the local load via storage and demand response techniques. In this study, the boat demand flexibility in the Ballen marina on Samsø—a medium-sized Danish island—is analysed for improving the local grid operation. For this purpose, suitable electricity tariffs for the marina and sailors are developed based on the conducted demand analysis. The optimal scheduling of boats and battery energy storage system (BESS) is proposed, utilising mixed-integer linear programming. The marina’s grid-flexible operation is studied for three representative weeks—peak tourist season, late summer, and late autumn period—with the combinations of high/low load and photovoltaic (PV) generation. Several benefits of boat demand response have been identified, including cost savings for both the marina and sailors, along with a substantial increase in load factor. Furthermore, the proposed algorithm increases battery utilisation during summer, improving the marina’s cost efficiency. The cooperation of boat flexibility and BESS leads to improved grid operation of the marina, with profits for both involved parties. In the future, the marina’s demand flexibility could become an essential element of the local energy system, considering the possible increase in renewable generation capacity—in the form of PV units, wind turbines or wave energy

Short-Term Heat Demand Prediction Using Deep Learning for Decentralized Power-To-Heat Solutions

Coupling power and heat sectors will contribute to the integration of renewable energy but also promotes decarbonization of heat sector. Better heat demand forecasting is required for reducing the costs and increasing the energy efficiency. The traditional forecasting methods faces difficulties due to the irregularities in data, computational costs and generalizing the output. Application of machine learning based data-driven techniques provides better accuracy in handling data with nonlinear characteristics. This paper proposes the application of Long Short Term Memory (LSTM) based model to forecast the heat energy consumption of a pool of residential consumers considering the real data set. The prediction accuracy is improved by using wavelet transform for data preprocessing and the proposed model gives a better performance.</p

Assessment of Energy Arbitrage Using Energy Storage Systems: A Wind Park’s Perspective

With the growing application of green energy, the importance of effectively handling the volatile nature of these energy sources is also growing in order to ensure economic and operational viability. Accordingly, the main contribution of this work is to evaluate the revenue potential for wind parks with integrated storage systems in the day-ahead electricity markets using genetic algorithm. It is achieved by the concept of flexible charging–discharging of the Energy Storage System (ESS), taking advantage of the widespread electricity prices that are predicted using a feedforward-neural-network-based forecasting algorithm. In addition, the reactive power restrictions posed by grid code that are to be followed by the wind park are also considered as one of the constraints. Moreover, the profit obtained with a Battery Energy Storage System (BESS) is compared with that of a Thermal Energy Storage System (TESS). The proposed method gave more profitable results when utilizing BESS for energy arbitrage in day-ahead electricity markets than with TESS. Moreover, the availability of ESS at wind park has reduced the wind power curtailment