Linear Energy System Research Articles

A linear reduced-order model for the activated sludge process for the integration into a mixed-integer linear energy system optimisation model

Conventional wastewater treatment plants consume significant amounts of electricity. The constant aeration of the wastewater in order to foster the growth of microorganisms or the pumping of wastewater are two examples for energy-intensive processes within a plant. Case studies have shown that switching off blowers and inlet pumps for a certain period of time is possible without a loss in water quality. This yields a potential for wastewater treatment plants to provide demand response (DR) to the power system and thereby increase overall system flexibility. So far, the DR potential has only been quantified for individual plants, while the effects of large-scale DR provision by the wastewater treatment sector for the power system have not yet been studied. One reason for this is the lack of optimisation models which include both the wastewater treatment process and the power system operation in sufficient detail. Our model tackles this gap in the literature by providing a reduced-order linear biochemical model for the activated sludge process within a WWTP that can be incorporated into an operational energy system model. The results show that the effluent concentrations are predicted well by the linear reduced-order model in comparison to the results of the Standard Activated-Sludge model No. 1 (ASM1). Potential model applications are the variation of the airflow rate within a certain range and the variation of liquid influent flow rate to the system, which is a result of electricity load shedding of the inlet pumps and the blowers connected to the activated sludge tank.

Dependence of the tunneling properties on an anisotropic factor in a graphene-like system

In analogy with the Klein tunneling in an isotropic linear energy graphene system, we reveal an anisotropic Klein tunneling in an anisotropic two dimensional (2D) linear energy system. The chirality and the absence of the back scattering leads to the Klein tunneling. The tunneling characteristics can be tuned by the barrier structure, the incident energy and angle, and more importantly the anisotropic parameter and different potential direction. The controllable Klein and side tunneling peaks can be understood by the carrier states and the phases in barrier/well regions. This interesting tunneling behaviors enrich the 2D tunneling properties and broaden the studies on the three-dimension tunneling features which will enlarge its electronic applications.

Global Potentials and Costs of Synfuels via Fischer–Tropsch Process

This paper presents the potentials and costs of synthetic fuels (synfuels) produced by renewable energy via PEM water electrolysis and the subsequent Fischer–Tropsch process for the years 2020, 2030, 2040, and 2050 in selected countries across the globe. The renewable energy potential was determined by the open-source tool pyGRETA and includes photovoltaic, onshore wind, and biomass. Carbon dioxide is obtained from biomass and the atmosphere by direct air capture. The potentials and costs were determined by aggregating minimal cost energy systems for each location on a state level. Each linear energy system was modelled and optimised by the optimisation framework urbs. The analysis focused on decentralised and off-grid synthetic fuels’ production. The transportation costs were roughly estimated based on the distance to the nearest maritime port for export. The distribution infrastructure was not considered since the already-existing infrastructure for fossil fuels can be easily adopted. The results showed that large amounts of synthetic fuels are available for EUR 110/MWh (USD 203/bbl) mainly in Africa, Central and South America, as well as Australia for 2050. This corresponds to a cost reduction of more than half compared to EUR 250/MWh (USD 461/bbl) in 2020. The synfuels’ potentials follow the photovoltaic potentials because of the corresponding low levelised cost of electricity. Batteries are in particular used for photovoltaic-dominant locations, and transportation costs are low compared to production costs.

A massively parallel interior-point solver for LPs with generalized arrowhead structure, and applications to energy system models

Linear energy system models are a crucial component of energy system design and operations, as well as energy policy consulting. If detailed enough, such models lead to large-scale linear programs, which can be intractable even for the best state-of-the-art solvers. This article introduces an interior-point solver that exploits common structures of energy system models to efficiently run in parallel on distributed-memory systems. The solver is designed for linear programs with doubly-bordered block-diagonal constraint matrix and makes use of a Schur complement based decomposition. In order to handle the large number of linking constraints and variables commonly observed in energy system models, a distributed Schur complement preconditioner is used. In addition, the solver features a number of more generic techniques such as parallel matrix scaling and structure-preserving presolving. The implementation is based on the solver PIPS-IPM. We evaluate the computational performance on energy system models with up to four billion nonzero entries in the constraint matrix—and up to one billion columns and one billion rows. This article mainly concentrates on the energy system model ELMOD, which is a linear optimization model representing the European electricity markets by the use of a nodal pricing market-clearing. It has been widely applied in the literature on energy system analyses in recent years. However, it will be demonstrated that the new solver is also applicable to other energy system models.

Cost Uncertainties in Energy System Optimization Models: A Quadratic Programming Approach for Avoiding Penny Switching Effects

Designing the future energy supply in accordance with ambitious climate change mitigation goals is a challenging issue. Common tools for planning and calculating future investments in renewable and sustainable technologies are often linear energy system models based on cost optimization. However, input data and the underlying assumptions of future developments are subject to uncertainties that negatively affect the robustness of results. This paper introduces a quadratic programming approach to modifying linear, bottom-up energy system optimization models to take cost uncertainties into account. This is accomplished by implementing specific investment costs as a function of the installed capacity of each technology. In contrast to established approaches such as stochastic programming or Monte Carlo simulation, the computation time of the quadratic programming approach is only slightly higher than that of linear programming. The model’s outcomes were found to show a wider range as well as a more robust allocation of the considered technologies than the linear model equivalent.

Endogenous implementation of technology gap in energy optimization models—a systematic analysis within TIMES G5 model

Evaluation of global diffusion potential of learning technologies and their timely specific cost development across regions is always a challenging issue for the future technology policy preparation. Further the process of evaluation gains interest especially by endogenous treatment of energy technologies under uncertainty in learning rates with technology gap across the regions in global regional cluster learning approach. This work devised, implemented, and examined new methodologies on technology gaps (a practical problem), using two broad concepts of knowledge deficit and time lag approaches in global learning, applying the floor cost approach methodology. The study was executed in a multi-regional, technology-rich and long horizon bottom-up linear energy system model on The Integrated MARKAL EFOM System (TIMES) framework. Global learning selects highest learning technologies in maximum uncertainty of learning rate scenario, whereas any form of technology gap retards the global learning process and discourages the technologies deployment. Time lag notions of technology gaps prefer heavy utilization of learning technologies in developed economies for early reduction of specific cost. Technology gaps of any kind should be reduced among economies through the promotion and enactment of various policies by governments, in order to utilize the technological resources by mass deployment to combat ongoing climate change.

The role of CH4 and N2O emission reductions in the cost-effective control of the greenhouse gas emissions from Finland

The reduction of carbon dioxide (CO2) emissions may be quite expensive and it is necessary to consider reduction measures for other anthropogenic greenhouse gases, such as methane (CH4) and nitrous oxide (N2O) as well. Their contribution to the total GHG emission from Finland is about 15–20%. In Finland most of the CH4 emissions are due to waste management, agriculture and burning processes. N2O emissions originate from burning processes, agriculture, industry and atmospheric deposition of nitrogen. The cost-effective reduction of the Finnish GHG emissions has been studied with the EFOM-ENV model, which is a quasi-dynamic linear energy system optimisation model. The target function to be minimised is the total discounted cost for the modelled system. In this study the model has been expanded to cover all well-known anthropogenic CO2, CH4 and N2O sources and reduction measures. The results indicate it is economic to reduce the emissions of CO2, CH4 and N2O in Finland. It is profitable to exploit the economic reduction potential of CH4 and N2O, because then the abatement of CO2 emissions does not need to be as extensive as when the reduction is aimed only at CO2 emissions. The inclusion of CH4 and N2O decreases the annual reduction costs about 20% in the year 2010.