Decentralized energy supply and electricity market structures

Small decentralized power generation units (DG) are politically promoted because of their potential to reduce GHG-emissions and the existing dependency on fossil fuels. A long term goal of this promotion should be the creation of a level playing field for DG and conventional power generation. Due to the impact of DG on the electricity grid infrastructure, future regulation should consider the costs and benefits of the integration of decentralized energy generation units. Without an adequate consideration, the overall costs of the electricity generation system will be unnecessarily high. The present paper analyses, based on detailed modelling of decentralized demand and supply as well as of the overall system, the marginal costs or savings resulting from decentralized production. Thereby particular focus is laid on taking adequately into account the stochasticity both of energy demand and energy supply. An efficient grid pricing system should then remunerate long-term grid cost savings to operators of decentralized energy production or/and charge long-term additional grid costs to these operators. With detailed models of decentralized demand and supply as well as the overall system, the marginal costs or savings resulting from decentralized production are determined and their dependency on characteristics of the grid and of the decentralized supply are discussed.electricity markets, decentralized power production, demand side management

Modelling the impact of different permit allocation rules on optimal power plant portfolios

The electricity generation mix of many European countries is strongly dominated by fossil fuelled power plants. Given that CO2-emissions are responsible for a major part of the anthropogenic greenhouse effect, emission trading has been introduced in the EU in 2005. Under the European emissions trading scheme (ETS), the emission quantities of major industry branches, most notably the electricity industry are capped and a system of tradable CO2 emission permits is established. Although the effects of emission trading on emissions, industry structure and investment had been analysed on beforehand by a number of models, the impact of rules for primary permit allocation has so far hardly been focused on. This was mostly seen as a distributional issue not affecting the efficiency of the market mechanism itself. However a closer look at the permit allocation rules shows that the number of permits allocated to new plants often depends on their fuel and technology (e. g. in Germany). This may consequently have distorting effects on market prices and investment decisions, which so far have been hardly investigated quantitatively. In order to analyse such effects, a mixed complimentary programming (MCP) model is developed, which allows to model investment incentives in the electricity sector. It takes into account major power generation technologies, emission constraints, endogenous investment allocation rules and price elasticity of demand. In particular also the time-varying structure of electricity demand is accounted for and the corresponding distinction of base- and peak-load technologies. The model is applied to the EU-27 focusing on the year 2015, i.e. on the third trading period, where so far no decision has been made on the allocation rules to be applied. From this analysis we derive the average market prices for emission allowances and electricity and the optimal power plant capacities under different allocation schemes. In a pure environmental perspective the auctioning of permits is expected to be a first-best solution, but it could endanger the competitiveness and the security of supply of the European Union. The reason for the latter is that the generation mix becomes biased in favour of gas fuelled plants, which are associated with the least specific CO2-emissions, but have to be imported to a large extent from politically unreliable regions like Russia or the Middle East. The results of our analysis however show that allocating emissions for free, based on expected full-load hours and fuel specifics, will lead to higher CO2-prices whilst the effect of securing supply is only limited. Also electricity prices will only be slightly lower, so that the contribution of free allocation schemes to economic competitiveness is also limited.climate protection, security of supply, emission trading, allocation of emission permits, electricity markets, power plant portfolio

Rapprochements ethnolinguistiques aux zones minières artisanales à l’est de la RD Congo

In the past two decades numerous academic studies have focused on artisanal mining governance in the eastern part of the Democratic Republic of Congo (DRC). Nevertheless, researchers, policy-makers and organizations that debate or engage in reforming the artisanal mining (ASM) sector in the region are often unaware of local terminology, which sometimes causes inaccurate interpretations. As these terminologies are non-codified, fluid, and evolving, they are often reserved exclusively to insiders, i.e. to those who are involved in ASM throughout the supply chain. This article collects terms used in and around mining sites in eastern DRC’s North Kivu and South Kivu provinces. Through a discussion of the terms and an analysis of their role and history in their respective contexts, it aims at contributing to a better understanding of the ASM sector. We begin with an ethno-linguistic analysis of the terms used by artisanal miners and other local stakeholders around the pits, during the processing, and at the stage of trading the minerals. Subsequently, we complement the first part with a discussion of the similarities and differences across our fieldwork sites and provide insight into the self-perception of artisanal miners and their work. In conclusion, we embed our linguistic findings in a wider contextualisation and a partial ethnography of everyday life in and around an economy often characterised by stereotypes

Semantic 3D Reconstruction with Finite Element Bases

We propose a novel framework for the discretisation of multi-label problems on arbitrary, continuous domains. Our work bridges the gap between general FEM discretisations, and labeling problems that arise in a variety of computer vision tasks, including for instance those derived from the generalised Potts model. Starting from the popular formulation of labeling as a convex relaxation by functional lifting, we show that FEM discretisation is valid for the most general case, where the regulariser is anisotropic and non-metric. While our findings are generic and applicable to different vision problems, we demonstrate their practical implementation in the context of semantic 3D reconstruction, where such regularisers have proved particularly beneficial. The proposed FEM approach leads to a smaller memory footprint as well as faster computation, and it constitutes a very simple way to enable variable, adaptive resolution within the same model