The optimal design of microgrids with thermal energy system requires optimization techniques that can provide investment and scheduling of the technology portfolio involved. In the modeling of such systems with seasonal storage capability, the two main challenges include the low temporal resolution of available data and the non-linear cost versus capacity relationship of solar thermal and heat storage technologies. This work overcomes these challenges by developing two different optimization models based on mixed-integer linear programming with objectives to minimize the total energy costs and carbon dioxide emissions. Piecewise affine functions are used to approximate the non-linear cost versus capacity behavior. The developed methods are applied to the optimal planning of a case study in Austria. The results of the models are compared based on the accuracy and real-time performance together with the impact of piecewise affine cost functions versus non-piecewise affine fixed cost functions. The results show that the investment decisions of both models are in good agreement with each other while the computational time for the 8760-h based model is significantly greater than the model having three representative periods. The models with piecewise affine cost functions show larger capacities of technologies than non-piecewise affine fixed cost function based models.

Mixed Integer Linear Programming (MILP) optimization algorithms provide accurate and clear solutions for Microgrid and Distributed Energy Resources projects. Full-scale optimization approaches optimize all time-steps of data sets (e.g., 8760 time-step and higher resolutions), incurring extreme and unpredictable run-times, often prohibiting such approaches for effective Microgrid designs. To reduce run-times down-sampling approaches exist. Given that the literature evaluates the full-scale and down-sampling approaches only for limited numbers of case studies, there is a lack of a more comprehensive study involving multiple Microgrids. This paper closes this gap by comparing results and run-times of a full-scale 8760 h time-series MILP to a peak preserving day-type MILP for 13 real Microgrid projects. The day-type approach reduces the computational time between 85% and almost 100% (from 2 h computational time to less than 1 min). At the same time the day-type approach keeps the objective function (OF) differences below 1.5% for 77% of the Microgrids. The other cases show OF differences between 6% and 13%, which can be reduced to 1.5% or less by applying a two-stage hybrid approach that designs the Microgrid based on down-sampled data and then performs a full-scale dispatch algorithm. This two stage approach results in 20–99% run-time savings.

Recently, researchers have begun to study hybrid approaches to Microgrid techno-economic planning, where a reduced model optimizes the DER selection and sizing is combined with a full model that optimizes operation and dispatch. Though providing significant computation time savings, these hybrid models are susceptible to infeasibilities, when the size of the DER is insufficient to meet the energy balance in the full model during macrogrid outages. In this work, a novel hybrid optimization framework is introduced, specifically designed for resilience to macrogrid outages. The framework solves the same optimization problem twice, where the second solution using full data is informed by the first solution using representative data to size and select DER. This framework includes a novel constraint on the state of charge for storage devices, which allows the representation of multiple repeated days of grid outage, despite a single 24-h profile being optimized in the representative model. Multiple approaches to the hybrid optimization are compared in terms of their computation time, optimality, and robustness against infeasibilities. Through a case study on three real Microgrid designs, we show that allowing optimizing the DER sizing in both stages of the hybrid design, dubbed minimum investment optimization (MIO), provides the greatest degree of optimality, guarantees robustness, and provides significant time savings over the benchmark optimization.

With energy systems, the problem of economic planning is decisive in the design of a low carbon and resilient future grid. Although several tools to solve the problem already exist in literature and industry, most tools only consider a single “typical year” while providing investment decisions that last around a quarter of a century. In this paper, we introduce why such an approach is limited and derive two approaches to correct this. The first approach, the Forward-Looking model, assumes perfect knowledge and makes investment decisions based on the full planning horizon. The second novel approach, the Adaptive method, solves the optimization problem in single year iterations, making incremental investment decisions that are dependant on previous years, with only knowledge of the current year. Comparing the two approaches on a realistic microgrid, we find little difference in investment decisions (maximum 21% difference in total cost over 20 years), but large differences in optimization time (up to 12000% time difference). We close the paper by discussing implications of forecasting errors on the microgrid planning process, concluding that the Adaptive approach is a suitable choice.

Computational time in optimization models scales with the number of time steps. To save time, solver time resolution can be reduced and input data can be down-sampled into representative periods such as one or a few representative days per month. However, such data reduction can come at the expense of solution accuracy. In this work, the impact of reduction of input data is systematically isolated considering an optimization which solves an energy system using representative days. A new data reduction method aggregates annual hourly demand data into representative days which preserve demand peaks in the original profiles. The proposed data reduction approach is tested on a real energy system and real annual hourly demand data where the system is optimized to minimize total annual costs. Compared to the full-resolution optimization of the energy system, the total annual energy cost error is found to be equal or less than 0.22% when peaks in customer demand are preserved. Errors are significantly larger for reduction methods that do not preserve peak demand. Solar photovoltaic data reduction effects are also analyzed. This paper demonstrates a need for data reduction methods which consider demand peaks explicitly.

Currently, many Microgrid projects remain financially uncertain and not bankable for institutional investors due to major challenges in existing planning and design methods that require multiple, complex steps and software tools.

Existing techniques treat every Microgrid project as a unique system, resulting in expensive, non-standardized approaches and implementations which cannot be compared. That is, it is not possible to correlate the results from different planning methods performed by different project developers and/or engineering companies.

This very expensive individual process cannot guarantee financial revenue streams, cannot be reliably audited, impedes pooling of multiple Microgrid projects into a financial asset class, nor does it allow for wide-spread and attractive Microgrid and Distributed Energy Resource projects deployment.

Thus, a reliable, integrated, and streamlined process is needed that guides the Microgrid developer and engineer through conceptual design, engineering, detailed electrical design, implementation, and operation in a standardized and data driven approach, creating reliable results and financial indicators that can be audited and repeated by investors and financers.

This article describes the steps and methods involved in creating bankable Microgrids by relying on an integrated Microgrid planning software approach that unifies proven technologies and tested planning methods, researched and developed by the United States National Laboratory System as well as the US Department of Energy, to reduce design times.

The present study examines the possible use of urban and rural traffic areas for producing biomass. Many of those areas (for example, parking lots at cinemas and shopping centers) are only intensively used during certain times. Most of the time those areas remain empty.
At the same time a major problem for large-scale implementation of renewable energy is the massive land use resulting from limited energy density of solar radiation and, in case of biomass production, low efficiency for utilization of solar radiation by plants. Additionally, renewable energies are often criticized for the fact that they require areas, which could also be used for food and feed production.
Therefore, it is an attractive idea to use some of the traffic areas that are lost for the ecosystem anyway for biomass production. This approach is novel that no data have been available yet. The aim of this work was therefore to develop technical solutions, to quantify the technical potential for this type of biomass production and, subsequently, for energy supply, based on data on the area utilization, climatic data and known properties of microalgae.
The work deals with the question of the technical potential for this approach in Austria. This question is
answered by a survey of the area data in Austria, the elaboration of technical systems for a possible implementation, as well as by calculating the biomass potential, based on simulation results. The data have been collected, analyzed and evaluated in a comprehensive literature search. The potential analysis provides an overview of the distribution of traffic areas in Austria and the resulting biomass potential. Thus, a list of possible areas including biomass and energy quantities is available.

The present study examines the possible use of urban and rural traffic areas for producing biomass. Many of those areas (for example, parking lots at cinemas and shopping centers) are only intensively used during certain times. Most of the time those areas remain empty.
At the same time a major problem for large-scale implementation of renewable energy is the massive land use resulting from limited energy density of solar radiation and, in case of biomass production, low efficiency for utilization of solar radiation by plants. Additionally, renewable energies are often criticized for the fact that they require areas, which could also be used for food and feed production.
Therefore, it is an attractive idea to use some of the traffic areas that are lost for the ecosystem anyway for biomass production. This approach is novel that no data have been available yet. The aim of this work was therefore to develop technical solutions, to quantify the technical potential for this type of biomass production and, subsequently, for energy supply, based on data on the area utilization, climatic data and known properties of microalgae.
The work deals with the question of the technical potential for this approach in Austria. This question is
answered by a survey of the area data in Austria, the elaboration of technical systems for a possible implementation, as well as by calculating the biomass potential, based on simulation results. The data have been collected, analyzed and evaluated in a comprehensive literature search. The potential analysis provides an overview of the distribution of traffic areas in Austria and the resulting biomass potential. Thus, a list of possible areas including biomass and energy quantities is available.

We briefly review the general concept and expected market potential of microgrids, then discuss the optimization challengesassociated with planning local cross-sectorial energy systems. A fair technology-neutral approach to this optimization task leads to a hard problem, which has to be tackled with advanced methods of mathematical optimization.The power of this approach is illustrated ina case study, concerning the replacement of heating systems in an alpine valley. In this case study we see both the potential for cost reduction and for the reduction of CO2emissions by an integrated planning approach.