Pumped-hydro in Bendigo: Room for wider reform?

As the world adjusts to a rapidly changing climate, interest in renewable energy technologies has increased. One disadvantage of renewables is that they’re only intermittently reliable; when the sun doesn’t shine, or the wind stops blowing the energy generation capacity of these infrastructures tapers off. In light of these changes there has been renewed interest in the creation of Pumped Hydro-Electricity Storage (‘PHES’), which acts as a ‘battery’ storing excess electricity generated when consumption of energy is at its lowest. In addition new technologies allow for legacy infrastructures, that would otherwise be a liability for the community, to now be used in a productive way for projects like PHES. Although there are many exciting opportunities the Bendigo PHES project presents, there is a dearth of regulatory issues for a small-medium scale project like this. This paper will explore the way in which such a project would fit within the national electricity market, as well as the limitations and barriers to implementation it might face before making some brief recommendations on changes that would benefit other such schemes in the future.     

The effects of fuel variability on the electrical performance and durability of a solid oxide fuel cell operating on biohythane

Biohythane is typically composed of 60/30/10 vol% CH₄/CO₂/H₂ and can be produced via two-stage anaerobic digestion of renewable and low carbon biomass with much greater efficiency compared with CH₄/CO₂ biogas. This work investigates the effects of fuel variability on the electrical performance and fuel processing of a commercially available anode supported solid oxide fuel cell (SOFC) operating on biohythane mixtures at 750 °C. Cell electrical performance was characterised using current-voltage curves and electrochemical impedance spectroscopy. Fuel processing was characterised using quadrupole mass spectroscopy. It is shown that when H₂/CO₂ is blended with CH₄ to make biohythane, the SOFC efficiency is significantly increased, high SOFC durability is achieved, and there are considerable savings in CH₄ consumption. Enhanced electrical performance was due to the additional presence of H₂ and promotion of CH₄ dry reforming, the reverse Boudouard and reverse water-gas shift reactions. These processes alleviated carbon deposition and promoted electrochemical oxidation of H₂ as the primary power production pathway. Substituting 50 vol% CH₄ with 25/75 vol% H₂/CO₂ was shown to increase cell power output by 81.6% at 0.8 V compared with pure CH₄. This corresponded to a 3.4-fold increase in the overall energy conversion efficiency and a 72% decrease in CH₄ consumption. A 260 h durability test demonstrated very high cell durability when operating on a typical 60/30/10 vol% CH₄/CO₂/H₂ biohythane mixture under high fuel utilisation due to inhibition of carbon deposition. Overall, this work suggests that decarbonising gas grids by substituting natural gas with renewably produced H₂/CO₂ mixtures (rather than pure H₂ derived from fossil fuels), and utilising in SOFC technology, gives considerable gains in energy conversion efficiency and carbon emissions savings.     

Malaysia’s electricity market structure in transition

The Malaysian electricity industry evolved from a vertically integrated utility into a single-buyer model, which considered a precursor to deregulation and a competitive market. This study considers electricity sector reform in Malaysia and proposes a market structure for the industry. We focus on the single-buyer model and the pool model for the analysis with the recommendation on reform stages required to ensure a smooth and successful market transition. A smooth transition from regulated market into deregulated market will ensure market stability and sustainability of the energy market. It will also provide enough time for the market participants to adapt and transit from regulated to deregulated energy market.     

Multi-stage power source and grid coordination planning method considering grid uniformity

Given the increasing uncertainties in power supply and load, this paper proposes the concept of power source and grid coordination uniformity planning. In this approach, the standard deviation of the transmission line load rate is considered as the uniformity evaluation index for power source and grid planning. A multi-stage and multi-objective optimization model of the power source and grid expansion planning is established to minimize the comprehensive cost of the entire planning cycle. In this study, the improved particle swarm optimization algorithm and genetic algorithm are combined to solve the model, thus improving the efficiency and accuracy of the solution. The analysis of a simple IEEE Garver’s 6-node system shows that the model and solution method are effective and feasible. Moreover, they are suitable for the coordinated planning of the power source and grid under a diversified nature of power supply and load.     

Modeling Smart Grid neighborhoods with the ENERsip ontology

The electricity consumption in the buildings sector has been steadily increasing during the last decade, up to the point that energy efficiency in this sector has become a major problem for governments, utilities, customers, and the environment. The foreseen high penetration of distributed micro-generation facilities based on renewables can help to reduce the environmental footprint of buildings and households, although the complexity of managing effectively the electric grid increases dramatically under these conditions. The IEEE 2030 standard for interoperability in the Smart Grid remarks upon the importance of well-defined data models in such complex scenarios and puts emphasis on the benefits of ontologies and OWL (Web Ontology Language) for this purpose. This paper presents an OWL-based ontology that formally defines the vocabulary and taxonomy and captures the engineering and business semantics of this domain of knowledge (i.e., energy efficiency in the so-called nZEN – nearly Zero-Energy Neighborhoods). This ontology has been defined under the scope of the EU (European Union) research project ENERsip. The paper also highlights the main benefits the ontology brought to all the phases of the project life cycle, as well as how future work can make the most out of it.     

An Advanced Universal Power Quality Conditioning System and MPPT method for grid integration of photovoltaic systems

A photovoltaic system usually produces power quality pollutions to the interfaced power systems without a proper functioning of active filters. At present, available active filters are unable to fully suppress power grid harmonics and distortions, worsening power quality conditions within the interfaced system. This paper presents an Advanced Universal Power Quality Conditioning System (AUPQS) to interface photovoltaic systems to a power grid. In the interfaced system, an improved series active filter is designed to generate output voltages to compensate all the source voltage deficiencies and to highly suppress the grid-end current harmonics and distortions, even under unbalanced non-linear load conditions. Also, an independent single-phase inverter is suggested at the load side instead of the source side to regulate the DC-link voltage. Hence, the distortions and harmonics generated by this single-phase inverter are absorbed by the shunt filter of the AUPQS. Finally, a hybrid maximum power point tracking (MPPT) approach is proposed for effective operation of photovoltaic systems even under sudden light intensity changes and solar cells temperature variations. The overall interfaced system is designed and developed using Matlab/Simulink software. The effectiveness of the proposed AUPQS and MPPT method is further validated by simulation results.     

Rules discovery in fuzzy classifier systems with PSO for scheduling in grid computational infrastructures

Particle swarm optimization (PSO) is a bio-inspired optimization strategy founded on the movement of particles within swarms. PSO can be encoded in a few lines in most programming languages, it uses only elementary mathematical operations, and it is not costly as regards memory demand and running time. This paper discusses the application of PSO to rules discovery in fuzzy classifier systems (FCSs) instead of the classical genetic approach and it proposes a new strategy, Knowledge Acquisition with Rules as Particles (KARP). In KARP approach every rule is encoded as a particle that moves in the space in order to cooperate in obtaining high quality rule bases and in this way, improving the knowledge and performance of the FCS. The proposed swarm-based strategy is evaluated in a well-known problem of practical importance nowadays where the integration of fuzzy systems is increasingly emerging due to the inherent uncertainty and dynamism of the environment: scheduling in grid distributed computational infrastructures. Simulation results are compared to those of classical genetic learning for fuzzy classifier systems and the greater accuracy and convergence speed of classifier discovery systems using KARP is shown.     

新能源大规模接入背景下的电网企业规划能力综合评价

当前,我国新能源电力飞速发展,新能源大规模的接入给电网企业规划带来了更大的挑战。电网规划研究对象多集中于电网本身,对电网企业规划研究较少。文章在该背景下构建电网企业规划能力评价指标体系,并在组合赋权的基础上建立改进物元可拓模型对实际算例展开电网企业规划能力综合评价。最终验证所提出的模型能在实际应用中有较强的推广性,能帮助电网企业更好地识别自身规划能力。     

基于改进灰色白化权函数的电网企业节能改造服务效益评价

电网企业配售电业务随着改革发展形势的变化,面临更强的市场竞争,用户对更加多元化、便捷化的能源电力服务的需求也愈加迫切,增值服务对于电网企业具有重要意义。以电网企业发展节能改造服务为例,通过构建相应指标体系及量化评估模型,实现对电网企业节能改造服务效益的综合评价,为电网企业评估节能改造服务的效益潜力提供决策支撑。     

Grid Search for Predicting Coronary Heart Disease by Tuning Hyper-Parameters

Diagnosing the cardiovascular disease is one of the biggest medical difficulties in recent years. Coronary cardiovascular (CHD) is a kind of heart and blood vascular disease. Predicting this sort of cardiac illness leads to more precise decisions for cardiac disorders. Implementing Grid Search Optimization (GSO) machine training models is therefore a useful way to forecast the sickness as soon as possible. The state-of-the-art work is the tuning of the hyperparameter together with the selection of the feature by utilizing the model search to minimize the false-negative rate. Three models with a cross-validation approach do the required task. Feature Selection based on the use of statistical and correlation matrices for multivariate analysis. For Random Search and Grid Search models, extensive comparison findings are produced utilizing retrieval, F1 score, and precision measurements. The models are evaluated using the metrics and kappa statistics that illustrate the three models’ comparability. The study effort focuses on optimizing function selection, tweaking hyperparameters to improve model accuracy and the prediction of heart disease by examining Framingham datasets using random forestry classification. Tuning the hyperparameter in the model of grid search thus decreases the erroneous rate achieves global optimization.