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Abstract: This paper investigates the technical and economic potential of energy-intensive industries to provide demand-side management (DSM) in electricity and balancing markets through 2030. Increasing shares of renewables will lead to a rising demand for ancillary services at the same time that less conventional plants will be available to provide these services. This paper makes projections on the extent to which DSM from industrial processes can provide economic benefits in electricity markets with renewables by providing tertiary reserve capacity. Different industrial processes and their specific technical and economic properties are investigated and compared with other storage devices and electricity generation technologies. Based on an extension of an existing European electricity market model, simulations are used here to make long-term forecasts for market prices, dispatch and investments in the electricity markets through linear optimization. [ABSTRACT FROM AUTHOR]

The rapid electrification of residential heating and mobility sectors is expected to drive the existing distribution grid assets beyond their planned operating conditions This change will also reveal new potentials through sector coupling, flexibilities, and the local exchange of decentralized generation. This paper thus presents an optimization framework for multi-modal energy systems at the low voltage (LV) distribution grid level. In this, we focus on the reinforcement requirements of the grid and the techno-economic assessment of flexibility components and coordination between agents. By employing a multi-level approach, computational complexity is reduced, and various levels of coordination and flexibilities are implemented. We conclude the work with a case study for a representative rural grid in Germany, in which we observe high economic potential in the flexible operation of buildings, majorly thanks to better integration of photovoltaics. Across all paradigms barring a best-case benchmark, grid reinforcement based on a mix of passive and active measures was necessary. A synergy effect is observed between flexibilities and coordination, as their combination reduces the peaks to the extent of completely avoiding grid reinforcement. The presented framework can be applied with a wide range of grid and component types to outline the broad landscape of future LV distribution grids. • Optimization framework for holistic planning of multi-modal LV distribution system. • Framework investigates grid reinforcement needs under flexibilities and coordination. • Framework is demonstrated with an exemplary rural distribution grid in Germany. • Synergy effects between flexibilities and coordination to integrate PV excess exist. • Flexibilities and coordination together reduce system costs by %25, and grid costs by 65%. [ABSTRACT FROM AUTHOR]

This paper analyses the effects of regional renewable electricity self-sufficiency targets on the power system in Germany. For this purpose, an interdisciplinary approach from social sciences and energy system modelling was chosen, which allows considering qualitative factors such as public acceptance or political stability. Following the concept of context scenarios, consistent raw scenarios are generated by a cross-impact balance analysis (CIB), and the scenarios are quantified by the unit commitment and expansion cost minimisation model ENTIGRIS considering power plants, storages, and the electricity grid. This approach enables an understanding of the system framework conditions and their relationships and allows the combination of qualitative and quantitative scenario descriptors. The most important factors for setting regional self-sufficiency targets were identified through interviews. The main system effects identified are: The regional distribution of generation capacities is strongly influenced by a more demand-oriented installation of generation capacities. This leads to less grid reinforcement, but higher rates of curtailment. In all scenarios, higher utilization of the PV roof potential instead of ground mounted could be observed. The total system costs are increasing only slightly with regional self-supply targets. In general, it was found that the influence of regional self-sufficiency targets is less pronounced in scenarios that already achieve high national RES shares than in scenarios that achieve lower shares, since technology, storage and grid expansion measures are necessary anyway to achieve high RES shares. Overall, the effects here are rather small and the regional objective is not associated with major disadvantages for the system. In a future characterised by stagnation, the system can benefit from regional targeting, as higher renewable shares and lower costs can result. The main conclusion therefore is that regional target setting seem to be beneficial for the overall power system, in terms of system cost, national RE share, acceptance and CO2-emissions. [ABSTRACT FROM AUTHOR]

Pursuing long-term climate stabilisation scenarios subjects power, heat, industry, and transport sectors to undergo fundamental transitions towards fully renewable energy systems. Cost-efficient strategies involve the integration of energy vectors, introducing additional cross-sectoral electricity demand and flexibility for traditional power sectors. When performing cross-sectoral capacity expansion planning (CEP) analyses of low-carbon energy systems for decision making support, the underlying mathematical optimisation problems easily become intractable. To overcome the computational challenges of optimising real-world instances of future energy systems with adequate representations of temporal, spatial, and technological detail, this work investigates different modelling approaches for flexible heat pump systems at the power and heat sector interface of the long-term energy system model SCOPE SD. A comparative case study carried out for a net-neutral decarbonisation scenario in Europe and Germany highlights the implications of using model aggregation techniques. It is shown that the proposed modelling approaches can present computational benefits at only limited cost of result accuracy. However, poorly implemented grouping strategies can lead to high result inaccuracies and even a lack of performance improvements. [ABSTRACT FROM AUTHOR]

Abstract: This paper examines the potential effects of Germany’s feed-in tariff policy for small roof-top solar PV systems installed between 2009 and 2030. Employing a partial equilibrium approach, we evaluate the policy by weighing the benefits from induced learning and avoided environmental externalities against the social costs of promoting residential PV. We use a dynamic optimization model that maximizes social welfare by accounting for learning-by-doing, technology diffusion, and yield-dependent demand. We find a wide range of effects on welfare, from net social costs of 2014million€ under a “business as usual” scenario to 7586million€ of net benefits under the positive prospects of PV’s development. Whereas the “business as usual” scenario underestimates actual price reductions, the positive scenario mirrors recent price developments and feed-in tariffs in the German residential PV market. [Copyright &y& Elsevier]

The problem discussed in this paper is motivated by the new recycling directive Waste Electrical and Electronic Equipment of the European Commission. The core of this law is, that each company which sells electrical or electronic equipment in a European country has the obligation to recollect and recycle an amount of returned items which is proportional to its market share. To assign collection stations to companies, in Germany for one product type a territory design approach is planned. However, in contrast to classical territory design, the territories should be geographically as dispersed as possible to avoid that a company, respectively its logistics provider responsible for the recollection, gains a monopoly in some region. First, we identify an appropriate measure for the dispersion of a territory. Afterwards, we present a first mathematical programming model for this new problem as well as a solution method based on the Greedy Randomized Adaptive Search Procedure methodology. Extensive computational results illustrate the suitability of the model and assess the effectiveness of the heuristic. [ABSTRACT FROM AUTHOR]

The estimation of hydrological model parameters is a challenging task. With increasing capacity of computational power several complex optimization algorithms have emerged, but none of the algorithms gives a unique and very best parameter vector. The parameters of fitted hydrological models depend upon the input data. The quality of input data cannot be assured as there may be measurement errors for both input and state variables. In this study a methodology has been developed to find a set of robust parameter vectors for a hydrological model. To see the effect of observational error on parameters, stochastically generated synthetic measurement errors were applied to observed discharge and temperature data. With this modified data, the model was calibrated and the effect of measurement errors on parameters was analysed. It was found that the measurement errors have a significant effect on the best performing parameter vector. The erroneous data led to very different optimal parameter vectors. To overcome this problem and to find a set of robust parameter vectors, a geometrical approach based on Tukey's half space depth was used. The depth of the set of N randomly generated parameters was calculated with respect to the set with the best model performance (Nash-Sutclife efficiency was used for this study) for each parameter vector. Based on the depth of parameter vectors, one can find a set of robust parameter vectors. The results show that the parameters chosen according to the above criteria have low sensitivity and perform well when transferred to a different time period. The method is demonstrated on the upper Neckar catchment in Germany. The conceptual HBV model was used for this study. [ABSTRACT FROM AUTHOR]

• Linear Program for designing bidirectional low temperature networks. • Holistic sizing approach for all building energy systems in a district. • Performance evaluation with economic, thermodynamic and ecologic indicators. • Comparison with stand-alone HVAC systems. • Bidirectional low temperature network result in substantial energy and cost savings. This paper presents a novel design methodology based on Linear Programming for designing and evaluating distributed energy systems with bidirectional low temperature networks (BLTNs). The mathematical model determines the optimal selection and sizing of all energy conversion units in buildings and energy hubs connected to the BLTN while minimizing total annualized costs. The optimization superstructure of building energy systems comprises heat pumps, compression chillers, heat exchangers for direct cooling, cooling towers and thermal energy storages. The design approach is applied to a real-world use case in Germany and the BLTN performance is compared to a reference case with individual HVAC systems. The BLTN concept shows a cost reduction of 42% and causes 56% less CO 2 emissions compared to individual HVAC systems. [ABSTRACT FROM AUTHOR]

The accuracy of computer numerical control machine tools can be improved by identifying error sources affecting the overall position error and orientation errors. Because of their inevitable nature, the position errors cannot be entirely eliminated from the machinery, but they can be identified, measured and compensated during the manufacturing process of the components by developing and using a mathematical model. In this present work, different mathematical models have been developed for the errors measured by laser interferometer at different nominal positions of X, Y and Z axes both in forward and reverse direction movement as per VDI 3441 Germany standard. Using Akaike information criterion, the best model is selected for each axis and later the best model's coefficients have been optimized by considering both minimizing sum square errors and maximizing R 2 values using teaching–learning-based optimization algorithm. Technique for Order Preference by Similarity to the Ideal Solution method has been adopted to convert the dual objectives into a single objective. An improvement of 1%–71% in R 2 values was reported to prove the effectiveness of the proposed optimization algorithm, Teaching–Learning-Based Optimization algorithm, with the same sum square error values. [ABSTRACT FROM AUTHOR]