Comparative assessment of control strategies for the biradial turbine in the Mutriku OWC plant

To be competitive against other renewable energy sources, energy converted from the ocean waves needs to reduce its associated levelised cost of energy. It has been proven that advanced control algorithms can increase power production and device reliability. They act throughout the power conversion chain, from the hydrodynamics of wave absorption to the power take-off to improve the energy yield. The present work highlights the development and test of several algorithms to control the biradial turbine which is to be installed in the Mutriku oscillating water column plant. A collection of adaptive and predictive controllers is explored and both turbine speed controllers and latching strategies are examined. A Wave-to-Wire model of one chamber of the plant is detailed and simulation results of six control laws are obtained. The controllers are then validated using an electrical test infrastructure to prepare the future deployment in the plant. Finally, the control strategies are assessed against criteria like energy production, power quality or reliability.This work has received funding from the European Union'sHorizon 2020 research and innovation programme under grantagreement No 654444 (OPERA Project). This work was financed by GV/EJ (Basque Country Government) under grants IT1324-19. The second author was partially funded by the Portuguese Foundationfor Science and Technology (FCT) through IDMEC, under LAETAPEst-OE/EME/LA0022 by FCT researcher grant No. IF/01457/2014.The authors acknowledge AZTI Tecnalia for wave resource data measured at the plant

Electricity demand forecasting for decentralised energy management

The world is experiencing a fourth industrial revolution. Rapid development of technologies is advancing smart infrastructure opportunities. Experts observe decarbonisation, digitalisation and decentralisation as the main drivers for change. In electrical power systems a downturn of centralised conventional fossil fuel fired power plants and increased proportion of distributed power generation adds to the already troublesome outlook for operators of low-inertia energy systems. In the absence of reliable real-time demand forecasting measures, effective decentralised demand-side energy planning is often problematic. In this work we formulate a simple yet highly effective lumped model for forecasting the rate at which electricity is consumed. The methodology presented focuses on the potential adoption by a regional electricity network operator with inadequate real-time energy data who requires knowledge of the wider aggregated future rate of energy consumption. Thus, contributing to a reduction in the demand of state-owned generation power plants. The forecasting session is constructed initially through analysis of a chronological sequence of discrete observations. Historical demand data shows behaviour that allows the use of dimensionality reduction techniques. Combined with piecewise interpolation an electricity demand forecasting methodology is formulated. Solutions of short-term forecasting problems provide credible predictions for energy demand. Calculations for medium-term forecasts that extend beyond 6-months are also very promising. The forecasting method provides a way to advance a novel decentralised informatics, optimisation and control framework for small island power systems or distributed grid-edge systems as part of an evolving demand response service

Optimising Energy Systems in Smart Urban Areas

In this chapter, the urban structure will be defined with zero or almost zero energy consumption, followed by pollution parameters. Energy systems are designed as networks of energy-intensive local hubs with multiple sources of hybrid energies, where different energy flows are collected on the same busbar and can be accumulated, delivered, or transformed as needed into the intelligent urban area. For analysis of the purpose function of our energy system, a micro-network of renewable energy sources (RES) is defined by penalization and limitations. By using fuzzy logic, a set of permissible solutions of this purpose function is accepted, and the type of daily electricity consumption diagrams is defined when applying cluster analysis. A self-organising neural network and then a Kohonen network were used. The experiment is to justify the application of new procedures of mathematical and informatics-oriented methods and optimisation procedures, with an outlined methodology for the design of smart areas and buildings with near zero to zero energy power consumption

Liquid air as an energy storage:A review

With the increasing demand for energy due to rapid industrialisation and the environmental concerns due to the usage of fossil fuels as the main energy source, there is a shift towards renewable energy. However, the intermittent nature of renewable energy requires energy produced during off-peak hours to be stored. This paper explores the use of liquefied air as an energy storage, the plausibility and the integration of liquefied air into existing framework, the role of liquefied air as an energy storage in addressing the Grand Challenges for Engineering as well as its employability in Malaysia