Search

Grid integrated water electrolysers have the potential of coupling electric power systems subjected to high shares of renewable energy sources with sectors of hydrogen demand, thus contributing to European decarbonization goals in future. We therefore investigate the business potential of future electrolyser applications in cross-commodity arbitrage trading by applying a complex power market simulation method for future scenarios and different European countries. Based on this, we evaluate the potential of additional provision of grid services towards grid operators in order to increase the electrolyser utilization ratio. For this, we use a method that identifies measures of transmission grid operators in order to ensure secure grid operation. In this context, uncertain hydrogen prices and different sectors of hydrogen demand are addressed through sensitivities of different hydrogen sales prices. The analysis shows a high dependency of business model efficiency on the hydrogen price. While cross-commodity arbitrage trading can achieve profitability for the transportation sector, applications for the industry sector and natural gas system are less efficient. The results however indicate that for these less efficient applications grid service provision can be an option of increasing the electrolyser utilization ratio thus increasing its profitability. [ABSTRACT FROM AUTHOR]

National climate protection goals require the emission reduction in municipal energy supply systems. Those systems include energy conversion units like heatings, energy storages, and grids for electricity, gas, and heat. This paper presents a mixed-integer linear optimization model for the integrated and multi-modal planning of such municipal energy systems to reach a defined CO 2 emission limit with the least-cost system design. The exemplary application of the model to a study case with 3900 buildings shows that the flexible hybrid energy system resulting without emission restrictions gradually transforms into an inflexible system solely relying on pellet heaters and photovoltaic units when tightening the emission limit by just a few percentage points. • Integrated and multi-modal design conception for municipal energy supply systems. • Optimization of energy conversion units, storages, and electric, heat, and gas grid. • Binary grid investment decisions, energy storages, and two-stage optimization added. • Economically optimal system combines multi-valent central and decentral supply. • Tightening of emission reduction target can lead to less flexible system design. [ABSTRACT FROM AUTHOR]

Highlights: [•] We develop an energy hub by wind, storage and demand response in distribution network. [•] We present two objective functions for the hub operation under certainty and uncertainty of wind, price and demand. [•] We consider operation costs, emission and reliability in the objective function. [•] We apply two stage stochastic programming of GAMS for solving the hub MILP model under uncertainty. [•] We evaluate effect of DERs and the uncertainties on hub operation costs and reliability in nine cases. [Copyright &y& Elsevier]

Energy System Optimization Models are applied to inform decision making processes and therefore must account for the uncertainty as it exists in the real world. In the recent decade, Modeling to Generate Alternatives gained popularity for considering structural uncertainty in Energy System Optimization Models. Modeling to Generate Alternatives finds numerous near-optimal alternatives, which could give the impression that all alternatives are comparably good. However, if one could compare near-optimal shadow prices of energy demand, certain alternatives would be superior to other solutions. Nevertheless, shadow prices of near-optimal solutions reflect the effort of being different and no approach to calculate expedient shadow prices of near-optimal solutions (comparable to the optimal one) has been presented. A simple approach is suggested and discussed with its implications, using an example scenario. The obtained shadow prices further differentiate the quality of solutions in the near-optimal solution space. Deeper analysis reveals regional or temporal bottlenecks of solutions and can help identify better search directions for alternative solutions. To some extent, the shadow prices highlight effects of parametric uncertainty, even though the proposed method cannot counteract this uncertainty. • Energy System Optimization Modeling benefits from analysis of near-optimal solutions. • Approach to enable expedient comparison of near-optimal shadow prices is presented. • Numerous near-optimal solutions are evaluated using said shadow prices. • Shadow prices differentiate the quality of Modeling to Generate Alternatives' solutions. [ABSTRACT FROM AUTHOR]

• Optimal power and gas flow problem considering power-to-gas. • Quasi-stationary modeling considering DC power flow and gas dynamics. • Scalable approach using successive linear programming. • Use case analyzing dispatch of renewable energy systems. The integrated modeling of power and gas systems gains importance as a result of increasing interdependencies due to bidirectional coupling in renewable energy systems through power-to-gas and gas-fired power plants. The optimal power and gas flow problem (OPGF) aiming to minimize system dispatch costs is mostly applied to small test systems. However, to investigate system-wide effects of sector coupling, large-scale transmission grids must be considered. Due to nonlinearities especially in the pressure loss equation, a nonlinear optimization problem results. This paper aims to demonstrate that successive linear programming, in contrast to other discussed approaches, is suitable for solving large-scale and multi-period OPGF-problems. The presented approach shows high solution quality despite short computing time even for large-scale systems such as the German power and gas transmission grid. [ABSTRACT FROM AUTHOR]

The energy system decarbonization leads to a decentralization of generation and flexibility. Among the new concepts to integrate distributed flexibility are local energy markets. While a broad range of research investigates local energy markets on a distribution grid level in detail, research on their system impact has been limited. Therefore, we develop a framework to investigate different configurations of local markets as well as the operation of distributed energy resources and their interaction with the wholesale markets. This framework is applied to a pan-European case study with detailed bottom-up modeling of local energy markets in France and Germany. Additionally, transmission grid simulations are carried out to investigate the impact of local trading on grid congestions. Our results show that a direct coordination of distributed flexibility within the wholesale market is more efficient than a preference for local trading. Nonetheless, local energy markets can be used as an incentive for the expansion of distributed flexibility as well as a way to break down the complexity of coordinating millions of distributed assets in a single market. Additionally, our results show that local trading relieves the transmission grid. • Bottom-up modeling of distributed flexibility in France and Germany. • Large-Scale-Modeling of LEMs. • Impact Assessment on Day-ahead market and transmission grid. [ABSTRACT FROM AUTHOR]

• Coupled steady-state and dynamic reactive power planning. • Assessment and planning of voltage control in intra-hour transitions. • Technology-dependent optimisation of active compensation devices to ensure short-term voltage stability. • Mixed-integer linear optimisation considering various voltage control requirements. Planning of reactive power compensation devices as part of the grid planning process is becoming increasingly important due to decreasing reactive power reserves from conventional power plants. Planning such devices requires the consideration of the reactive power demand both during steady-state operation and disturbances as well as gradual power-flow shifts. To control the voltage in these scenarios, a portfolio comprising location, rated power and technology of reactive power compensation devices must be identified. In this paper, a planning approach with a coupled determination of steady-state and dynamic reactive power demand is applied to a generic transmission system. Different characteristic grid use cases are analysed for the identification of the steady-state reactive power demand. The determination of the dynamic demand involves the analysis of critical power-flow shifts within an hourly generation time series. Shifts leading to insufficient voltage stability reserves and high voltage gradients are considered in the planning approach. Furthermore, critical line-outage and short-circuit scenarios are analysed in connection with an increasing demand for dynamic reactive power compensation. Results emphasise that the coupled determination of steady-state and dynamic reactive power demand identifies suitable portfolios of different compensation devices using synergies for voltage control during steady-state operation, disturbances and power-flow shifts. [ABSTRACT FROM AUTHOR]

Integrating increasing shares of weather-dependent renewable energies into energy systems while maintaining high levels of security of supply constitutes a challenge for system operators. The system integration of large shares of variable renewable energy generation requires an effective operation of electricity grids and efficient coordination among grid operators. Hence, the detailed modelling of grid operation has increasingly become important in recent years. Methods for modelling the operation of (extra) high-voltage grids are undergoing persistent enhancements in academia and industry. Existing approaches vary in data granularity and computational methods. Moreover, assumptions on technical details in grid models vary. The results are thus subject to differences in input data and modelling methods. The paper at hand aims to identify the most relevant differences that exist amongst grid simulation models and methods for studying congestion management in a European context. Differences are studied based on a comparison of grid simulation models from eight German energy modelling institutions. The effects of model parameterization and formulation on congestion management results are investigated based on three different case studies focusing on outage simulation, line constraint relaxation and the modelling of cross-border measures. Results indicate that data parametrization can have large impacts on model results with respect to congestion management volumes and the geographic distribution of the respective measures. The analysis also highlights the need to thoroughly calibrate key model parameters. The research findings will assist the grid modelling community and power system planners in simulating congestion management and increase the validity and explorative power of grid simulation models. • Various differences in model formulations for grid operation exist in academia. • Soft-constraints are widely used to detect data inconsistencies and infeasibilities. • Simplified outage simulations particularly impact congestion results in meshed grids. • Parametrization of cross-border measures largely affects congestion volumes. [ABSTRACT FROM AUTHOR]

Abstract: Feeder reconfiguration is one of the most important tasks for loss reduction and reliability improvement in distribution networks. Most of studies so far have investigated reconfiguration problem as a static problem considering fixed level of loads. This assumptions lead to suboptimal solution because of time-varying nature of loads in distribution networks. The switching operation should be cost effective and the reconfiguration scheme should balance the benefits in system loss reduction and reliability improvement against the costs of switching. Moreover, this is a dynamic problem and switching operations of time intervals over a year are not independent. This paper presents a method to determine annual feeder reconfiguration scheme considering switching costs and time-varying variables such as load profiles. In the first stage of the proposed method, to obtain effective configurations, optimal configuration for each day of year is determined independently using harmony search algorithm (HSA) and graph theory. After determination of effective configurations for the network, in the second stage, year is divided into multi equal periods and considering loss cost, interruption cost and also switching cost from a configuration to another configuration, dynamic programming algorithm (DPA) is used to find the optimum annual reconfiguration scheme. The proposed method has been tested on 95-bus distribution network and the obtained results denote that to have an optimum solution it is necessary to compare operation costs dynamically. [Copyright &y& Elsevier]

• Integrated optimization of multi-stage expansion planning and grid operation. • Selection of expansion candidates via linear programming relaxations. • Optimizing the grid operation reduces the need for network expansion. • Need for new lines is substituted by the placement of phase shifting transformers. • Minimization of expansion costs by dynamic multi-stage expansion planning. The ongoing transformation of the power system characterized by an increased share of renewable energy sources requires additional transmission capacities. Nevertheless, the need for new transmission capacities can be reduced by optimizing grid operation. This contribution presents a novel approach to the co-optimization of multi-stage transmission expansion planning and grid operation within a mixed-integer linear programming formulation. The expansion via AC systems and phase shifting transformers (PSTs) is combined with the control of PSTs and the determination of congestion management interventions such as redispatch of power plants or curtailment of renewable energies. The number of potential expansion candidates is reduced by linear programming relaxations. Exemplary results based on a synthetic test system containing 120 buses evidence the impact of the proposed approach on overall system costs. The proposed co-optimization reduces the total costs by 16.5 % when applied instead of the sole expansion via AC technology. [ABSTRACT FROM AUTHOR]