Panagiotis Fragkos

Incorporating Uncertainty into World Energy Modelling: the PROMETHEUS Model

In general, policy and most economic decisions like investments are formulated in a non-deterministic context. Their analysis can be considerably enhanced if probability information on future outcomes is available, especially in terms of unbiased estimates of the extent of unpredictability and stochastic dependence. For the sake of transparency, it is also important to be able to trace the justification of variability and its structure. This paper introduces PROMETHEUS, a stochastic model of the world energy system that is designed to produce joint empirical distributions of future outcomes concerning many variables that are important in terms of the evolution of the world energy system. The model methodology is based on Monte Carlo techniques, and the joint distributions of the model inputs are derived to a large extent not only from statistical econometric analysis but also from specialised studies. The emphasis is placed on the exhaustive coverage of variability including omitted variables. By incorporating detailed coverage of uncertainty into a comprehensive large-scale global energy system model, PROMETHEUS can be used to quantify probabilistic assessments of future model outcomes, which constitute critical parameters in formulating robust energy and climate policies. The description of the main model characteristics is complemented with an analytical example that illustrates the usefulness of stochastic PROMETHEUS results in the context of power generation investments under uncertainty.

Assessment of lidargrammetry for spatial data extraction

Lidargrammetry concerns the production of inferred stereopairs (ISPs) from LiDAR intensity images, intended to stereodigitize spatial data in digital photogrammetric stations. The production of ISPs is based on the principle of stereoorthomates and the extraction of a derivative intensity image, in which an artificial x-parallax is being introduced; other techniques have also developed in order to best utilize the 3D nature of LiDAR data. Lidargrammetry is a relatively new approach, not yet assessed properly, in order to quantify its derivative spatial data quality and the impact of its reduced photointerpretative ability, comparing to typical photogrammetric stereomodels. In this paper, a dense point cloud of 55 points/m2 is used, which is thinned out to 25 points/m2 and 7 points/m2 in order to simulate scan missions of lower pulse repetition frequency. ISPs are being produced from each of these point cloud’s intensity images using the slope parallel projection method and building footprints are being extracted. Using the denser point cloud’s footprints as control data, the relative accuracy of the thinner point cloud’s footprints is assessed, in order to evaluate the effect of the decreasing resolution in the digitization process. Estimated footprint’s relative accuracy (2σ) is 0.5m and 1m for the 25 points/m2 and the 7 points/m2 clouds respectively. Moreover, a reference footprint dataset was derived, by a stereorestitution procedure, using high resolution optical aerial images. Absolute spatial accuracy ranges around 1.5 m making the Lidargrammetric technique capable for extracting spatial data suitable even for large scale mapping.

Cost effective accurate orthoimaging of inaccessible areas

Although high resolution orthoimages have been the most popular photogrammetric derivative, the cost of orthoimaging production remains high for areas where access is difficult. This paper presents a procedure for a cost effective orthoimage production, from data (digital images, GCPs and DTMs) derived solely from Airborne Laser Scanner systems. ALS systems are designed to produce a highly accurate 3D point cloud, which can be easily rasterized in an accurate, high resolution and dense DEM. Applying a hillshade effect on that dense DEM, one can visualize the objects with great detail and thus measure 3D coordinates for points to be used as GCPs. Modern ALS systems incorporate medium format digital cameras with high resolution imaging abilities. As no ground surveying is needed, the production of high end orthoimages may be performed even in regions that are inaccessible or difficult to access, such as remote islets and mountain tops. A workflow is proposed for the production of orthoimages from low density (2.5 points/m2) laser data using an ALS II-50 system and its medium format RCD105 digital camera. The area of the case study is the northern section of the Greek island Milos. Utilizing automated processes, a series of quality control tasks and a preprocessing of the data is performed. During the data processing, an interpolation of the LiDAR data’s first returns is performed, in order to rasterize a high resolution (1m) DEM. The orthorectification process is been performed with the same DEM, leading to an orthoimage with a planimetric accuracy of 1m and minimum geometric distortions. The cost analysis of the applied procedure has proved that the method is less cost demanding compared to the usual orhoimage production methods.

Energy system transitions and macroeconomic assessment of the Indian building sector

ABSTRACT India’s energy sector has grown rapidly in recent years with buildings playing a major role as they constitute about 40% of India’s final energy demand. This paper provides a quantitative model-based assessment of the evolution of India’s building sector in terms of both energy systems transition and its macroeconomic implications. The coupling of a bottom-up technology-rich energy system model with a macroeconomic computable general equilibrium (CGE) model provides an innovative approach for the in-depth robust analysis of the energy transition in India’s building stock and the induced macroeconomic and employment impacts on the Indian economy. Two main scenarios are explored, namely: the business-as-usual (BAU) and the advanced nationally determined contribution (Adv. NDC) scenarios. The investigation shows that efficiency improvements are vital to counteract the upward pressure on energy demand in the building sector. Energy demand in the building sector results in an increase of CO2 emissions by 27% between 2015 and 2030 due to the technology transition from inefficient solid fuels (traditional biomass) to cleaner energy (liquefied petroleum gas (LPG), piped natural gas (PNG)) before shifting to electricity. The Adv. NDC scenario also leads to a shift in employment from agriculture and towards sectors that benefit from the implementation of Adv. NDC, especially in the construction sectors, electricity and manufacturing sectors.

Investments in Power Generation Under Uncertainty—a MIP Specification and Large-Scale Application for EU

Investments in power generation constitute a typical budget allocation problem in the context of multiple objectives, while all factors influencing investor’s decisions for power plants are subject to considerable uncertainties. The paper introduces a multi-objective stochastic model designed to optimize budget allocation decisions for power generation in the context of risk aversion taking into account several sources of uncertainty, especially with regard to volatility of fossil fuel and electricity prices, technological costs, and climate policy variability. Probability distributions for uncertain factors influencing investment decisions are directly derived from the stochastic global energy model PROMETHEUS and thus they take into account complex interactions between variables in the systemic context. In order to fully incorporate stochastic characteristics of the problem, the model is specified as an optimization problem in which the probability that an objective exceeds a given threshold is maximized (risk aversion) subject to a set of deterministic and probabilistic constraints. The model is formulated as a mixed integer program providing complete flexibility on the joint distributions of rates of return of technologies competing for investments, as it can handle non-symmetric distributions and take automatically into account complex covariance patterns as emerging from comprehensive PROMETHEUS stochastic results. The analysis shows that risk is a crucial factor for power generation investments with investors not opting for technologies subject to uncertainty related to climate policies and fossil fuel prices. On the other hand, combination of options with negative covariance tends to benefit in the context of risk-hedging behavior.

Analysis of Future Common Strategies Between the South and East Mediterranean Area and the EU in the Energy Sector

Abstract Uncontestably the south and east Mediterranean (SEM) region and the European Union (EU) will mutually benefit by sharing energy resources in view of their strategic long-term aspirations. The SEM region has great potential for energy efficiency improvements and deployment of renewables which are sufficient both to cover local needs and to release hydrocarbon resources for exportation as well as to help the EU achieve the Energy Roadmap emission reduction targets at lower costs. Two kinds of obstacles currently hamper taking advantage of the untapped potential: (1) lack of power interconnection infrastructure and regulatory policy framework and (2) persistence of fossil fuel subsidies in a large part of the SEM region. This analysis is based on a large-scale energy system modeling of both the SEM and the EU regions and assesses two alternative cooperation strategies. The first strategy is essentially based on centralized actions involving large-scale exploitation of renewables in large-scale installations with a mainly exportation orientation. This strategy offers the best, cost-effective prospects for electricity exports to the EU under mechanisms of remuneration based on power purchase agreements (PPAs) and geographic expansion of the EU-ETS. The second, alternative strategy gives priority to decentralized investment aiming at exploiting dispersed renewable sources – the renewables recombined with proactive removal of current energy-pricing distortions and policies providing incentives for energy savings by energy consumers. The two scenarios differ in technology mix and have different impacts on the energy system, the energy trade balance, power generation costs, and investment financing. The scenarios also require different regulatory and investment promoting frameworks to materialize.