Renewable energies curtailment induced by grid congestions increase due to grown renewable energies integration and the resulting mismatch of grid expansion. Short-term predictions for curtailment can help to increase the efficiency of its management. This paper proposes a novel, holistic approach of a short-term curtailment prediction for distribution grids. The load flow calculations for congestion detection are realized by taking different operational security criteria into account, whereas the models for the node-injections are adjusted to the characteristic of each grid node specifically. The determination of required curtailment based on the resulting congestions considers uncertainties of component loading and its corresponding probability. The forecast model is validated using an actual 110 kV distribution grid located in Germany. In order to meet the requirements of a forecast model designed for operational business, prediction accuracy, and its greatest source of error are analyzed. Furthermore, a suitable length of training data is investigated. Results indicate that a six month time period for maintenance gains the highest accuracy. Curtailment prediction accuracy is better for transmission system operator components than for distribution system operator components, but the Sørensen Dice factor for the aggregated grid shows a high match of historic and predicted curtailment with a value of 0.84 and a low error for curtailed energy, which makes 2.23% of the historic curtailed energy. The model is a promising approach, which can contribute to improvement of curtailment strategies and enable valuable insight into distribution grids.
Let F be either the set of all bounded holomorphic functions or the set of all m-homogeneous polynomials on the unit ball of ℓ r . We give a systematic study of the sets of all u ∈ ℓ r for which the monomial expansion α ∂ α f (0) α! u α of every f ∈ F converges. Inspired by recent results from the general theory of Dirichlet series, we establish as our main tool, independently interesting, upper estimates for the unconditional basis constants of spaces of polynomials on ℓ r spanned by finite sets of monomials.We use standard notation from Banach space theory. As usual, we denote the conjugate exponent of 1 ≤ r ≤ ∞ by r ′ , i.e. 1 r + 1 r ′ = 1. Given m, n ∈ N we consider the following sets of indices M (m, n) = j = ( j 1 , . . . , j m ) ; 1 ≤ j 1 , . . . , j m ≤ n = {1, . . . , n} mFor indices i, j ∈ M we denote by (i, j) = (i 1 , i 2 , . . . , j 1 , j 2 , . . . ) the concatenation of i and j. An equivalence relation is defined in M (m) as follows: i ∼ j if there isGiven a Banach sequence space X and some index subset J ⊂ J , we write P ( J X ) for the closed subspace of all holomorphic functionswhere z j for j = ( j 1 , . . . , j ℓ ) stands for the monomial z j : u → u j := u j 1 ·. . . ·u j ℓ . For J ⊂ J (m), we call J * = j ∈ J (m − 1); ∃k ≥ 1, (j, k) ∈ J the reduced set of J .
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.
The growing integration of renewable energies into electricity grids leads to an increase of grid congestions. One countermeasure is the curtailment of renewable energies, which has the disadvantage of wasting energy. Forecasting congestion provides valuable information for grid operators to prepare and instruct countermeasures to reduce these energy losses. This paper presents a novel approach for congestion prediction in distribution grids (i.e. up to 110 kV) considering the n-1 security criterion. For this, our method considers node injections and power flow and combines three artificial neural network models. The analysis of study results shows that the implemented neural networks within the presented approach perform better than naive forecasts models. In the case of vertical power flow, the artificial neural networks also show better results than comparable parametric models: average values of the mean absolute errors relative to the parametric models range from 0.89 to 0.21. A high level of accuracy can be achieved for the neural network that predicts the loading of grid components with a F1 score of 0.92. Further, also with a F1 score of 0.92, this model shows higher accuracy for the distribution grid components than for those of the transmission grid, which achieve a F1 score of 0.84. The presented approaches show good potential to support grid operators in congestion management.
This work outlines an approach to the optimal design optimization of a photovoltaic (PV) and battery storage system and its integration into the sectorintegrated energy system of a logistics company's facilities. Another major objective is the optimized integration of refrigerated trailers (reefers) into the energy system with the goal of minimizing both costs and CO 2 emissions, as demonstrated in a case study. For this purpose, an existing energy system model utilizing reefers was optimized for computing time and the energy system was extended through the use of a facility's cooling utility. Multi-criteria design optimization was performed using a multi-objective evolutionary algorithm based on decomposition (MOEDA/D) approach. For this, three key performance indicators (KPIs) were used: the annuity, CO 2-emissions, and own-consumption rate. The results of the multi-criteria design optimization were then analyzed using Pareto fronts. Stakeholders are thus able to find their individual techno-economic/ecological optimum and so plan the transformation to an decentralized, renewable, distributed energy supply accordingly. Three selected Pareto optimal results were selected to evaluate the effect of PV and battery storage on the optimal operation of the sector-integrated energy system and reefer integration.
Decarbonization requires new energy systems components to mitigate fossil fuel dependency, for instance electric vehicles and heat pumps, forming a sector integrated energy system. Energy management is a promising approach to integrate these devices more efficiently by orchestrating the respective consumption and generation. This study investigates the advantage of an advanced energy management algorithm that is applied to varying energy system scenarios. The energy management algorithm is based on economic principles and the system topology is represented by a rooted tree. Grid elements form parents, which act as auctioneers and devices act according to type specific demand and supply functions. This algorithm is compared to an approach where devices are not coordinated, at a system scale of six households. In order to account for different characteristics of the energy system, the different scenarios are defined according to a morphological analysis and are analysed by means of Monte-Carlo simulation. These scenarios vary the PV generation, heating technology, and building insulation. It is shown that the algorithm reduces peak loads across all scenarios by around 15 kW. Other key performance indicators, such as own consumption and self-sufficiency show a dependency on the scenarios, although the algorithm outperforms the reference in each one, achieving an increase in own consumption of at least 13 p.p. and 22 p.p. in terms of self-sufficiency.INDEX TERMS Energy management, Monte-Carlo methods, scenario analysis, systems modelling I. LIST OF ABBREVIATIONS
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