Forecasting methods in energy planning models

Energy planning models (EPMs) play an indispensable role in policy formulation and energy sector development. The forecasting of energy demand and supply is at the heart of an EPM. Different forecasting methods, from statistical to machine learning have been applied in the past. The selection of a forecasting method is mostly based on data availability and the objectives of the tool and planning exercise. We present a systematic and critical review of forecasting methods used in 483 EPMs. The methods were analyzed for forecasting accuracy; applicability for temporal and spatial predictions; and relevance to planning and policy objectives. Fifty different forecasting methods have been identified. Artificial neural network (ANN) is the most widely used method, which is applied in 40% of the reviewed EPMs. The other popular methods, in descending order, are: support vector machine (SVM), autoregressive integrated moving average (ARIMA), fuzzy logic (FL), linear regression (LR), genetic algorithm (GA), particle swarm optimization (PSO), grey prediction (GM) and autoregressive moving average (ARMA). In terms of accuracy, computational intelligence (CI) methods demonstrate better performance than that of the statistical ones, in particular for parameters with greater variability in the source data. However, hybrid methods yield better accuracy than that of the stand-alone ones. Statistical methods are useful for only short and medium range, while CI methods are preferable for all temporal forecasting ranges (short, medium and long). Based on objective, most EPMs focused on energy demand and load forecasting. In terms geographical coverage, the highest number of EPMs were developed on China. However, collectively, more models were established for the developed countries than the developing ones. Findings would benefit researchers and professionals in gaining an appreciation of the forecasting methods, and enable them to select appropriate method(s) to meet their needs

Decarbonization cost of Bangladesh's energy sector: Influence of corruption

As a rapidly developing lower-middle income country, Bangladesh has been maintaining a steady growth of +5% in the gross domestic product (GDP) annually since 2004, eventually reaching 7.1% in 2016. The country is targeting to become uppermiddle- income and developed by 2021 and 2041 respectively, which translates to an annual GDP growth rate of 7.58% during this period. The bulk of this growth is expected to come from the manufacturing sector, the significant shift towards which started at the turn of this century. Energy intensity of manufacturing-based growth is higher, the evidence of which can be seen in the 3.17 times increase in national energy consumption between 2001 and 2014. Also, Bangladesh aims to achieve 100% electrification rate by 2021 against an annual population growth rate of 1.08%. With the increasing per capita income, there is now a growing middle class fuelling the growth in demand for convenient forms of energy. Considering the above drivers, the Bangladesh 2050 Pathways Model suggested 35 times higher energy demand than that of 2010 by 2050. The government and private sector have started a substantial amount of investments in the energy sector to meet the signi ficant future demand. Approximately US$104 billion would be invested in the power sector of Bangladesh for establishing 33 GW installed capacity by 2030, the majority of which would be financed by national and international loans. However, Bangladesh is one of the most corrupted country in the world which may influence the energy planning development. The current policies of Bangladesh power sector paved the future direction towards predominantly coal-based energy mix which would augment the greenhouse gas (GHG) emissions five times (117.5 MtCO2e) in 2030 than that of 2010. By increasing GHG emissions, the country would undermine the worldwide effort of keeping global temperature rise in 21st century below 2°C, as per the Paris agreement and COP21. V The objective of this research was to develop a framework to explore the cost of decarbonizing the Bangladesh's energy sector by 2050. For the study, six emissions scenarios business as usual (BAU), current policy (CPS), high-carbon (HCS), medium-carbon (MCS), low-carbon (LCS) and zero-carbon scenarios (ZCS), and three economic conditions high, average and low costwere considered. The combination of emissions and economic scenarios rendered 18 different emissionseconomic scenarios for the research. The results showed that Bangladesh would emit 343 MtCO2e by 2050 without any emissions reduction strategies under HCS. However, Bangladesh can reduce 23 of HCS by adopting decarbonization strategies such as energy mix change towards renewable and nuclear. On the optimistic side, the emissions can be reduced 73 by 2050 under ZCS than that of HCS. The study demonstrated that a zero carbon future is not yet feasible for Bangladesh by 2050 because the operational fossil fuel based plants would be operational. Therefore, the GHG emissions are going to rise even if Bangladesh adopts renewables and nuclear dominating energy mix. However, it will be possible to keep the GHG emissions approximately 2 tCO2e/capita threshold if the country adopts LCS. On the other hand, only MCS and LCS can meet the projected energy demand by 2050. The energy sector can meet the projected demand under ZCS only if the electricity consumption is reduced 26 2050. In terms total cost, the MCS was found to be 3.9 LCS by 2050. LCS would have a higher cost than that of MCS up to 2030, due to the high capital cost of renewable technologies. The total cost under LCS would start to be lower than of MCS after 2035 for the fossil fuel cost. Accumulated fuel cost would reach$250 billion in 2050 under HCS, which can be reduced 23% under ZCS. The cost of decarbonization would be 3.6, 3.4 and 3.2 times under average cost of MCS, LCS, and ZCS, than that of HCS. As the energy sector of Bangladesh is under rapid development, the accumulated capital would be comparatively high by 2050. However, fuel cost can be significantly reduced under LCS and ZCS which would also ensure lower emissions. The study suggested that energy mix change, technological maturity, corruption and demand reduction can influence the cost of decarbonization. However, the most significant influencer for the decarbonization of Bangladeshi energy sector would be the corruption. Results showed that if Bangladesh can minimize the effect of corruption on the energy sector, it can reduce the cost of decarbonization 45-77% by 2050 under MCS, LCS, and ZCS

PV System Design and Performance

Photovoltaic solar energy technology (PV) has been developing rapidly in the past decades, leading to a multi-billion-dollar global market. It is of paramount importance that PV systems function properly, which requires the generation of expected energy both for small-scale systems that consist of a few solar modules and for very large-scale systems containing millions of modules. This book increases the understanding of the issues relevant to PV system design and correlated performance; moreover, it contains research from scholars across the globe in the fields of data analysis and data mapping for the optimal performance of PV systems, faults analysis, various causes for energy loss, and design and integration issues. The chapters in this book demonstrate the importance of designing and properly monitoring photovoltaic systems in the field in order to ensure continued good performance

Control of Proton Exchange Membrane Fuel Cell System

265 p.In the era of sustainable development, proton exchange membrane (PEM) fuel cell technology has shown significant potential as a renewable energy source. This thesis focuses on improving the performance of the PEM fuel cell system through the use of appropriate algorithms for controlling the power interface. The main objective is to find an effective and optimal algorithm or control law for keeping the stack operating at an adequate power point. Add to this, it is intended to apply the artificial intelligence approach for studying the effect of temperature and humidity on the stack performance. The main points addressed in this study are : modeling of a PEM fuel cell system, studying the effect of temperature and humidity on the PEM fuel cell stack, studying the most common used power converters in renewable energy systems, studying the most common algorithms applied on fuel cell systems, design and implementation of a new MPPT control method for the PEM fuel cell system