Search

The high share of power generation based on fluctuating renewable energy sources, especially wind and solar, has increased the levels of variability and uncertainty in power systems. The aim of this study is to develop a method for quantifying the variability of photovoltaic (PV) systems. The developed method investigates the power measurements of a PV system and quantifies its power and energy fluctuations in three steps. The first includes a classification of days into three classes according to the variability and power output of the PV system. The second consists of computing the empirical cumulative distribution functions of PV power ramps for the given classes. The third calculates the PV daily energy fluctuations based on predicted PV power output. This method of PV variability quantification was then applied to seven rooftop PV systems in different locations that feature different installed capacities, years of installation, orientations and solar cell types. The testing of this method indicates that it can be used to quantify the power and energy fluctuations of different PV systems, independent of their locations and technical characteristics. The proposed method was developed as part of a general approach for quantifying the flexibility potential of buildings and city districts with PV systems. [ABSTRACT FROM AUTHOR]

Especially in the energy supply of multi-family houses, a wide variety of stakeholders are involved, from owners, to users, to energy service providers and society. They usually have different requirements and understandings of optimality, but ultimately have to make joint decisions and thus sensible compromises. In Germany in particular, there are a large number of multi-family houses and, at the same time, many government restrictions and subsidies in terms of energy supply. This makes it difficult to make clear recommendations for the choice of an energy supply concept that takes all stakeholder interests into account. We first identify the relevant stakeholders and define their objectives. In order to relate these with one another, we present a methodology based on energy system simulation and TOPSIS to make energy concepts objectively evaluable. A generic multi-family house with 40 residential units is examined, combining different energy technologies and insulation standards. There is no energy concept that satisfies all stakeholders equally and it is difficult to build coalitions between them. The best results are achieved by air-source heat pumps in combination with photovoltaic. [ABSTRACT FROM AUTHOR]

The increasing share of volatile renewable energy generation in current and future energy systems which does not follow the demand, makes it necessary to shift the synchronization tasks to the system and demand level. The intended demand side consumption management is usually incentivized via financial mechanisms. However, the optimum design of related incentives is still unclear. It is usually discussed under the term "system friendliness" which in turn still lacks a broadly accepted definition. In this article we introduce a new technical definition of "system friendliness" and develop comprehensive and holistic related indicators. For that, we introduce the burden concept for energy systems which represents the amount of technical infrastructure required for stable operation. We use the concept of a hypothetical system energy storage to derive indicators that are capable of quantifying the system friendliness of a given point-of-interest. The developed indicators are independent of regulations or market environments which enables comparability of results between studies and different systems. In a case study we demonstrate our newly developed methodology and exemplary examine decentral district energy storages. Our findings demonstrate that today's regulatory environments that usually support residential self-consumption maximization are counterproductive. Instead, we show that dynamic end-user prices coupled with dynamic feed in tariffs are able to incentivize an almost 100% system friendly behavior of distributed prosumers. • Technical system friendliness in energy systems is defined based on the "burden" concept. • Holistic related indicators for sustainable energy systems are developed. • A case study examines system friendliness of residential districts in Germany 2050. • Residential self-consumption maximization is found to be counterproductive. • Variable feed-in tariffs incentivize almost 100% system friendly behavior. [ABSTRACT FROM AUTHOR]

In this study, we developed a technology-independent method for quantifying the time-varying flexibility potential of different energy systems. As the flexibility of these systems was assumed to be an additional service, their primary application must not be undermined by flexibility provision; for example, providing flexibility from a heat pump must not threaten the space heating of a building. Therefore, the method developed for quantifying flexibility contains an estimation of the technology- and schedule-specific boundaries that consider the primary application of the energy systems. The time-varying flexibility potential of energy systems was proposed to be presented in a universal, two-dimensional, and technologically-agnostic form. It enabled to develop a method for aggregating the flexibility values from different energy systems. The developed methods were demonstrated on two case studies: the first included a calculation of the flexibility potential of a single battery storage (BS) system in a private household, and the second presented aggregation of the flexibility from multiple BS systems. The simulation proved that these BS systems could have provided flexibility additionally to their operation in compliance with the boundary values. In both case studies, the BS systems exhibited significant daily and seasonal variations in flexibility potential depending on the actual mode, operation in the following hours, local energy generation, and consumption. In general, the developed methods can be utilised to quantify and aggregate the time-varying flexibility potentials of energy systems, alongside their scheduled operation in the course of a single day as well as across seasons. • The methods for quantifying and aggregating the time-varying flexibility of energy systems are proposed. • The energy flexibility is quantified considering the primary application of the energy systems. • The calculated flexibility is presented by a universal, two-dimensional and technologically-agnostic form. • The developed methods and forms are demonstrated on battery storage systems in residential buildings. [ABSTRACT FROM AUTHOR]

Solar cells used in building integration of photovoltaic cells ( BIPV) are commonly made from crystalline wafer cells. This contribution investigates the challenges and benefits of using bifacial solar cells in vertical installations. We show that those cells get up to 13% more irradiance compared to optimum tilted south facing monofacial modules in Germany. The role of the n-layer in thin amorphous bifacial single-junction cells intended to be used as bifacial cells in BIPV applications is investigated. In contrast to the superstrate cell design, a transparent n-layer and back contact play a key role to achieve high bifaciality. We therefore increased the transparency of the n-layer by adding CO2, increasing the PH3 flow in the deposition gas and tested different thicknesses. With those measures, we reached a bifaciality of 98% for short-circuit current density and 99% for open-circuit voltage. [ABSTRACT FROM AUTHOR]

The optimal combination of energy conversion and storage technologies with local energy demand is a key but in its result not obvious challenge of distributed energy. Although a variety of possible approaches to the optimal design of limited technology selections can be found in the literature, the previous design step, the actual technology selection, and the subsequent step, the selection of the optimal operating strategy, are often neglected. We develop and demonstrate a methodology, which can optimise energy systems with arbitrary technology selection and under multi-criteria optimality definitions. The energy system modelled in oemof.solph is optimised using a MOEA/D approach with regard to economic, ecological and technical key performance indicators. The aim is to find trends and tendencies with a methodology that is as generalised as possible in order to integrate it into the decision-making process in energy system planning. We demonstrate the method by means of a German district for which an integrated supply concept is being sought. Different evaluation and visualisation possibilities are presented and the chances and limitations of the developed methodology are identified. We show that not only the choice of technology, but especially its sizing and operational strategy have a decisive influence on the optimality. [Display omitted] • An optimal design methodology for use in energy system decision making is introduced. • The methodology utilises a simulation software based on oemof.solph and pygmo. • Methodology and software are demonstrated using the example of a district. • Various evaluation and analysis tools are demonstrated. • Trends and recommendations are elaborated for the further decision-making process. [ABSTRACT FROM AUTHOR]

• Description of a device agnostic, multi sectoral energy management algorithm. • Adaptation of the Japanese auction mechanism for energy management. • Simulations for an energy system with device specific, non-linear bidding behaviour. The energy system is undergoing structural changes due to the integration of volatile electricity generation technology and sector coupling. This article proposes an adapted continuous Japanese auction for device agnostic energy management for a sector coupled energy system. The auction mechanism ensures that no device specific information has to be exchanged. The algorithm is implemented for a single household containing a PV-system, a battery storage, an electric vehicle, a domestic hot water system, and a household load. For every component, a demand/supply function is proposed, which respects the respective physical characteristics. The algorithm is simulated for one year and compared to a reference, which represents an individual control of the devices without considering coordination. Further, two different auctioneers are proposed, one which focuses on energy efficient use of local resources and another one aims to reduce peak load. The results show that the proposed algorithm decreases the peak powers of the investigated system and improves local power usage, i.e. own consumption and self-sufficiency. Thus, this algorithm can reduce grid reinforcement demand due to lower peak powers by better usage of local resources, while satisfying sector coupling needs. [ABSTRACT FROM AUTHOR]

Load-forecasting problems have already been widely addressed with different approaches, granularities and objectives. Recent studies focused not only on deep learning methods but also on forecasting loads on single building level. This study aims to research problems and possibilities arising by using different load-forecasting techniques to manage loads. For that behavior of two neural networks, Long Short-Term Memory and Feed-Forward Neural Network as well as two statistical methods, standardized load profiles and personalized standardized load profiles are analyzed and assessed by using a sliding-window forecast approach. The results show that personalized standardized load profiles (MAE: 3.99) can perform similar to deep learning methods (for example, LSTM MAE: 4.47). However, because of the simplistic approach, load profiles are not able to adapt to new patterns. As a case study for evaluating the support of load-forecasting for applications in energy management systems, the integration of charging stations into an existing building is simulated by using load-forecasts to schedule the charging procedures. It is shown that forecast- based controlled charging can have a significant impact by lowering overload peaks exceeding the house connection point power limit (controlled charging 20.24 kW; uncontrolled charging: 65.15 kW) while slightly increasing average charging duration. It is concluded that integration of high flexible loads can be supported by using forecast-based energy management systems with regards to their limitations. [ABSTRACT FROM AUTHOR]

A fully automated transferable predictive approach was developed to predict photovoltaic (PV) power output for a forecasting horizon of 24 h. The prediction of PV power output was made with the help of a long short-term memory machine learning algorithm. The main challenge of the approach was using (1) publicly available weather reports without solar irradiance values and (2) measured PV power without any technical information about the PV system. Using this input data, the developed model can predict the power output of the investigated PV systems with adequate accuracy. The lowest seasonal mean absolute scaled error of the prediction was reached by maximum size of the training set. Transferability of the developed approach was proven by making predictions of the PV power for warm and cold periods and for two different PV systems located in Oldenburg and Munich, Germany. The PV power prediction made with publicly available weather data was compared to the predictions made with fee-based solar irradiance data. The usage of the solar irradiance data led to more accurate predictions even with a much smaller training set. Although the model with publicly available weather data needed greater training sets, it could still make adequate predictions. [ABSTRACT FROM AUTHOR]

The decarbonization of the energy system will bring substantial changes, from supranational regions to residential sites. This review investigates sustainable energy supply, applying a multi-sectoral approach from a residential site perspective, especially with focus on identifying crucial, plausible factors and their influence on the operation of the system. The traditionally separated mobility, heat, and electricity sectors are examined in more detail with regard to their decarbonization approaches. For every sector, available technologies, demand, and future perspectives are described. Furthermore, the benefits of cross-sectoral integration and technology coupling are examined, besides challenges to the electricity grid due to upcoming technologies, such as electric vehicles and heat pumps. Measures such as transport mode shift and improving building insulation can reduce the demand in their respective sector, although their impact remains uncertain. Moreover, flexibility measures such as Power to X or vehicle to grid couple the electricity sector to other sectors such as the mobility and heat sectors. Based on these findings, a morphological analysis is conducted. A morphological box is presented to summarize the major characteristics of the future residential energy system and investigate mutually incompatible pairs of factors. Lastly, the scenario space is further analyzed in terms of annual energy demand for a district. [ABSTRACT FROM AUTHOR]

• Energy demand estimation of refrigerated trailer (reefer) preconditioning processes. • Modeling distributed sector-integrated energy considering reefers. • Optimized electrical integration of reefers into distributed energy systems. • Optimization of costs, CO 2 emissions, PV self-consumption and load peaks. • Normalized results for estimating saving potentials of electric preconditioning. A model for the electric integration of temperature-controlled transports into decentralized, sector-integrated energy systems of logistics facilities is presented herein. A method for modeling an energy systems interaction with the energy components of a refrigerated trailer (reefer) is also introduced. Furthermore, the optimized integration of all sectors was demonstrated using the open energy modeling framework (oemof). For this purpose, the electricity, heat and transport sectors of a typical logistics company's premises are considered in the context of a case study, spanning the period of one year. Energy costs, CO 2 emissions, load peaks, and PV own-consumption are used to evaluate the gradual conversion of reefer preconditioning (pre-cooling and -heating) from diesel fuel into electrical grid supply. A linear solver optimizes the energy system in terms of costs and facilitates the optimized integration of reefers and the sector-integrated energy system, incorporating load peaks and CO 2 emissions. The results of the study demonstrate the great potential of reducing costs and emissions through the electrical integration of different numbers of refrigerated trailers and furthermore show the challenges and implications of this for other sectors. Based on the results of the optimization, normalized preconditioning operations were created and divided by setpoint, such as for transferability and comparability. Substituting diesel with electricity for the preconditioning of refrigerated trailers reduces costs and CO 2 emissions: 0.89 € and 1.56 kgCO 2 can be saved per normalized electric preconditioning process compared to diesel preconditioning, depending on the setpoint it can be more or less. This positive effect can be enhanced by making optimal use of decentralized PV. The use of battery storage increases self-consumption and reduces load peaks at the grid connection point, thus enabling the optimized integration of preconditioned temperature-controlled transports into sector-integrated energy systems. With optimized use of the battery storage and PV self-consumption, the savings compared to diesel can be increased to 1.12 € and 2.11 kgCO 2 per normalized preconditioning process. [ABSTRACT FROM AUTHOR]

We present a method to turn the results of model-based optimisations into resilient and comprehensible control strategies. Our approach is to define priority lists for all available technologies in a district energy system. Using linear discriminant analysis and the results of the optimisations, these are then assigned to discrete time steps using a set of possible steering parameters. In contrast to the model-based optimisations, the deduced control strategies do not need predictions or even perfect foresight but solely rely on data about the present. The case study using priority lists presents results in terms of emissions and prices that are only about 5% off the linear optimum. Considering that the priority lists only need information about the present, the results of the control strategies obtained using the proposed method can be considered competitive. [ABSTRACT FROM AUTHOR]

In this study, a new algorithm is developed to extract device profiles in a fully unsupervised manner from three-phases reactive and active aggregate power measurements. The extracted device profiles are then applied to disaggregate the aggregate power measurements by means of particle swarm optimization. Then, a new approach to very short-term power predictions is presented, which makes use of the disaggregation data. For this purpose, a state change forecast is carried out for each device by an artificial neural network and subsequently converted into a power prediction by reconstructing the power profile with respect to the state changes and device profiles. The forecast horizon is 15 min. In order to demonstrate the developed approaches, three-phase reactive and active aggregate power measurements of a multi-tenant commercial building are employed as a case study. The granularity of the data used is 1 s. In total, 52 device profiles are extracted from the aggregate power data. The disaggregation exhibited a highly accurate reconstruction of the measured power with an energy percentage error of approximately 1 %. The indirect power prediction method developed is then applied to the measured power data and outperforms the two persistence forecasts, as well as an artificial neural network designed for 24h-ahead power predictions working in the power domain. • We propose a disaggregation and prediction algorithm, without any requirement of labeled data. • The unsupervised device profile extraction requires only aggregate consumption data. • An application of energy disaggregation with particle swarm optimization is shown. • Indirect power prediction is performed, using state data from the disaggregation. • The power prediction is benchmarked with persistence predictions and a long-term reference. [ABSTRACT FROM AUTHOR]

There are 1454 district heating systems in Germany. Most of them are fossil based and with high temperature levels, which is neither efficient nor sustainable and needs to be changed for reaching the 2050 climate goals. In this paper, we present a case study for transforming a high to low temperature district heating system which is more suitable for renewable energy supply. With the Carnot Toolbox, a dynamic model of a potential district heating system is simulated and then transformed to a low temperature supply. A sensitivity analysis is carried out to see the system performance in case space constrains restrict the transformation. Finally, an economic comparison is performed. Results show that it is technically possible to perform the transformation until a very low temperature system. The use of decentralized renewable sources, decentralized heat storage tanks and the placement of a heat pump on each building are the key points to achieve the transformation. Regarding the sensitivity analysis, the transformation is worth doing until the seasonal storage and solar collector field sizes are reduced to 60% and 80% of their values in the reference case, respectively. The economic analysis shows, however, that it is hard for highly efficient low temperature renewable based heat networks to compete with district heating systems based on a centralized fossile CHP solution. Thus, though the presented transformation is technically possible, there is a strong need to change existing economic schemes and policies for fostering a stronger promotion of renewable energy policies in the heat sector. [ABSTRACT FROM AUTHOR]

Declining prices on frequency containment reserve (FCR) markets endanger the profitability of battery energy storage systems (BESS). BESS combined with power-to-heat units could improve the economics both by supplying higher power rates on FCR markets and by converting excessive power into heat. Two cases were investigated with a techno-economic model using primary operation and market data of 2018/2019. The system amortises after 12 years with a net present value of two million € operating on the FCR market. No improvement was realized by additional arbitrage trading. Taxes, levies and charges frameworks are crucial for the economic success of hybrid systems. • A hybrid battery and power-to-heat system has been economically investigated. • This hybrid system amortises in 12 years on the German FCR market. • Hybrid systems are more profitable than standalone batteries. • Additional arbitrage trading does not increase the profitability of hybrid systems. • Diffusion of sector integrating systems is limited by taxes, levies and charges. [ABSTRACT FROM AUTHOR]

The planning and decision-making for a distributed energy supply concept in complex actor structures like in districts calls for the approach to be highly structured. Here, a strategy with strong use of energetic simulations is developed, the core elements are presented, and research gaps are identified. The exemplary implementation is shown using the case study of a new district on the former Oldenburg airbase in northwestern Germany. The process is divided into four consecutive phases, which are carried out with different stakeholder participation and use of different simulation tools. Based on a common objective, a superstructure of the applicable technologies is developed. Detailed planning is then carried out with the help of a multi-objective optimal sizing algorithm and Monte Carlo based risk assessment. The process ends with the operating phase, which is to guarantee a further optimal and dynamic mode of operation. The main objective of this publication is to present the core elements of the planning processes and decision-making framework based on the case study and to find and identify research gaps that will have to be addressed in the future. [ABSTRACT FROM AUTHOR]