Hybrid Energy Grid Research Articles

A Hybrid Energy System Based on Externally Fired Micro Gas Turbines, Waste Heat Recovery and Gasification Systems: An Energetic and Exergetic Performance Analysis

The opportunities related to the adoption of synthetic gaseous fuels derived from solid biomass are limited by the issues caused by the peculiarities of the syngas. The aim of this paper is to analyze several possible layouts of hybrid energy systems, in which the main thermal source is the organic fraction of municipal solid wastes. The case of a small community of about 1000 persons is analyzed in this paper. The examined layouts coupled an externally fired micro gas turbine with a waste heat recovery system based on both an Organic Rankine Cycle and supercritical CO2 gas turbines. A thermodynamic analysis has been carried out through the use of the commercial software Thermoflex 31, considering the losses of each component and the non-ideal behavior of the fluids. The results of the numerical analysis highlight that the introduction of a waste heat recovery system leads to an increase of at least 16% in the available net power, while a cascade hybrid energy grid can lead to a power enhancement of about 29%, with a considerable increase also in the energetic and exergetic global efficiencies.

Performance Analysis of WHR Systems for Marine Applications Based on sCO2 Gas Turbine and ORC

Waste heat recovery (WHR) can represent a solution to improve the efficiency of ships’ propulsion, helping to exceed stringent greenhouse gas emission limits. This is particularly suitable in the case of propulsion based on gas turbines due to their medium-high temperature level of the exhaust gases. This study analyzes the performance of a hybrid energy grid, in which the heat is recovered by the exhaust gases of an aeroderivative gas turbine, a GE LM2500+, when the bottoming system is a supercritical CO2 gas turbine. Given the issues and peculiarities related to the onboard installation, where size and weight are fundamental concerns, six WHR schemes have been analyzed. They span from the simple cycle to partial preheated and regenerative, to a cascade layout in which an ORC system receives thermal power by the sCO2 GT. The influence of the seawater temperature on the performance of the hybrid energy system has been also considered. The energetic and exergetic performance comparison of the different schemes has been carried out by using the commercial software Thermoflex. The results showed that an increase in overall performance by up to 29% can be obtained and that the increase in seawater temperature can lead to a decrease in the overall performance.

Efficient daily solar radiation prediction with deep learning 4-phase convolutional neural network, dual stage stacked regression and support vector machine CNN-REGST hybrid model

Optimal utilisation of the sun's freely available energy to generate electricity requires efficient predictive models of global solar radiation (GSR). These are necessary to provide solar energy companies an early and effective market entry to support renewable energy integration into electrical grids. We propose a hybrid deep learning CNN-REGST method where a Convolutional Neural Network is integrated with a dual-stage Stacked Regression (Level-O Learner and Level-O predictor) followed by a Support Vector Machine (Level-1 Learner) with its hyperparameters optimised using the HyperOpt function to predict the daily GSR with high accuracy. Six solar energy farms in Queensland, Australia, are selected as testing sites and the predictive features from Global Climate Models and observations, derived using marine predator algorithm, are employed to build the CNN-REGST prediction model. We include a feature selection process based on meta-heuristic methods to select the optimal predictors used as inputs for the resulting CNN-REGST model. Our hybrid model is rigorously evaluated to analyze its performance over a yearlong, and all four season data. We also compare the proposed CNN-REGST model with several deep learning (i.e., CNN, Long-term Short-term Memory Network LSTM, Deep Neural Network DNN) and conventional ML approaches (Extreme Learning Machine ELM, Stacked Regression REGST, Random Forest Regression RFR, Gradient Boosting Machine GBM, Multivariate Adaptive Regression Splines MARS) using the same test datasets. The simulations carried out show that the proposed hybrid model is significantly accurate in GSR predictions compared with the deep learning and the ML models as well as a commonly used persistence model. We conclude that the CNN-REGST prediction model could be a useful scientific ploy incorporated in modern solar energy monitoring technologies to utilize a greater proportion of sustainable energy resources captured from the sun into consumer electricity for conventional-renewable hybrid energy grid systems.

Energy and exergy analysis of prosumers in hybrid energy grids

ABSTRACTSurplus energy can be a recurrent phenomenon in zero-energy buildings (ZEBs) with onsite generation systems, usually resulting in the export of excess electricity. Yet, converting electricity into heat and exporting it could improve the overall energy balance. This study analyses the energy and exergy performance of a Finnish nearly zero-energy building (nZEB) as a heat and electricity prosumer, and proposes alternative energy topologies to improve energy and exergy levels, primary energy demand and CO2 emissions. The results show that increasing the installed capacity of the photovoltaic systems would lead to zero energy, exergy, emissions and a balance of primary energy. However, by instead using the surplus electricity to drive a heat pump and export heat, the currently installed capacity would lead to a net energy export of over 4000 kWh/a. Thus, energy conversion could significantly enhance the contribution from heat and electricity prosumers to smart energy grids, though not without affecting other criteria. Two management strategies arise: favouring heat export improves the net energy and CO2 emissions reduction but lessens the net exergy, while favouring electricity export improves the net exergy and primary energy reduction. The findings highlight that energy conversion can enhance nZEB performance and its exchange with hybrid grids.

A dynamic model of a 100 kW micro gas turbine fuelled with natural gas and hydrogen blends and its application in a hybrid energy grid

The paper deals with the development of a dynamic model of a commercial 100 kW Micro Gas Turbine (MGT) fuelled with mixtures of standard (i.e. natural gas or methane) and alternative fuels (i.e. hydrogen). The model consists of a first-order differential equation (ODE) describing the dominant dynamics of the MGT imposed by its own control system during production electrical power. The differential equation is coupled to a set of nonlinear maps derived numerically from a detailed 0D thermodynamic matching model of the MGT evaluated over a wide range of operating conditions (i.e. mechanical power, fraction of hydrogen and ambient temperature). The efficiency of the electrical machine with power inverter and power absorbed by auxiliary devices is also taken into account. The resulting model is experimentally validated for a sequence of power step responses of the MGT at different ambient conditions and with different fuel mixtures.The model is suited for simulation and control of hybrid energy grids (HEGs) which rely on advanced use of MGT and hydrogen as energy carrier. In this regard, the MGT model is used in the simulation of an HEG based on an appropriate mix of renewable (non-programmable) and non-renewable (programmable) energy sources with hydrogen storage and its reuse in the MGT. Here, the MGT is used as a programmable energy vector for compensating the deficits of renewable energies (such as solar and wind) with respect to user demand, while excess renewable energy is used to produce hydrogen via electrolysis of water. The simulated HEG comprises a solar PhotoVoltaic (PV) plant (300 kW), an MGT (100 kW) fuelled with natural gas and hydrogen blends, a water electrolyzer (WE) system (8 bar, 56 Nm3/h), a hydrogen tank (54 m3), and an Energy Management Control System (EMCS).

ICT-Challenges in Load Balancing across Multi-Domain Hybrid Energy Infrastructures / IKT-Herausforderungen bei der Lastverschiebung in domänenübergreifenden Hybridnetzen

Summary Addressing the “storage gap” in future energy systems with a high penetration of renewable and volatile resources is a major challenge. Hybrid Energy Grids represent a possible solution to this problem by interconnecting energy systems and infrastructures and thus allowing energy to be transformed between various alternative forms of energy. This provides the means for higher flexibility along the energy system. The paper discusses the concept of Hybrid Energy Grids from an ICT perspective and presents a regulation framework to assess use cases in multi-domain Hybrid Energy Infrastructures.

複合エネルギーネットワークにおけるGGSの集合運用に関するゲーム論的分析

複合エネルギーネットワークにおけるGGSの集合運用に関するゲーム論的分析