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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.

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.

The limited reserve of fossil fuels and public awareness of environmental issues prompt the rapid development of renewable energy generation. However, the centralised utilisation of renewable energy in bulk power systems is impeded mainly by its volatile nature and transmission congestion, leading to the spillage of renewable power. The energy storage unit is expected to be a promising measure to smooth the output of renewable plants and reduce the curtailment rate. This study addresses the energy storage sizing problem in bulk power systems. To capture the operating status of the power system more accurately, the authors use a dedicated power flow model which involves voltage and reactive power. The uncertainty of renewable generation is described via inexact probability distributions encapsulated in a data‐driven Wasserstein‐metric based ambiguity set, based on which the renewable energy curtailment rate is formulated as a distributionally robust chance constraint. The objective is to minimise the total investment cost, and the optimal sizing problem gives rise to a distributionally robust chance‐constrained program, and is reformulated as a tractable linear program via conservative approximation. Case studies conducted on the modified IEEE 30‐bus and 118‐bus systems demonstrate the effectiveness and performance of the proposed approach. [ABSTRACT FROM AUTHOR]

Nowadays, achieving operational solutions to boost the flexibility of bulk power systems has become one of the major challenges in both industry and academia. The outcomes of recent studies demonstrate that the deployment of large-scale energy hubs can help enhance the flexibility of power systems. However, centralized management of networked energy hubs may not be compatible with the power system operator when they are managed by private owners. Motivated by this observation, a privacy-preserving decision-making structure is proposed in this paper for the collaborative operation of private industrial energy hubs and renewable power system by considering the high penetration of renewable energy sources. The proposed structure is drawn up based on the decentralized two-stage robust–stochastic approach and solved using the Benders decomposition algorithm by relying on the private ownership of various entities. The main objectives of this study lie in (1) decreasing renewable power curtailment and (2) minimizing the total operation costs of the private entities. To achieve these objectives, the effects of the multi-energy demand response program and energy conversion facilities are investigated in the context of the developed model. The competency and robustness of the proposed collaborative decision-making structure are examined on the IEEE 30-bus test system using GAMS and DIgSILENT PowerFactory software. Results show that if industrial energy hubs are successfully deployed in industrial parks, the total operation cost of the renewable power system decreases by up to 16.33%, renewable power curtailment reduces by 92.9%, and flexibility of the renewable power system enhances by increasing spinning reserve. • A privacy-preserving model is proposed for collaborative operation of private units. • A distributed approach is designed to coordinate power trading among various units. • The impacts of novel strategies are examined in reducing renewable power curtailment. • A robust–stochastic approach is used to address various uncertainties. • DIgSILENT is used to validate the results obtained from the optimization process. [ABSTRACT FROM AUTHOR]

Nowadays, the high penetration of renewable energy sources (RESs) with non-uniformly distributed patterns has created unprecedented challenges for regional power systems to maintain system flexibility and reliability. These technical challenges obligate power system operators to curtail part of the produced renewable energy at various scheduling intervals. Motivated by these challenges, grid-connected energy hubs are seen as a way forward to boost system flexibility, decrease the rate of renewable power curtailment, and increase energy efficiency. However, simplified models may significantly affect the performance of the grid-connected energy hubs in practice. Hence, this paper proposes a holistic structure to determine the optimal coordinated operation of the grid-connected energy hubs and the regional power system by relying on the high penetration of wind power. In this regard, various fundamental challenges that have not yet been addressed in an integrated manner, including the C O 2 emission rate and the amount of curtailed renewable energy along with total operating costs of the integrated energy system, are among the main objectives of the optimization problem. The proposed structure is developed in the form of tractable mixed-integer nonlinear programming (MINLP) problem to handle the day-ahead security-constrained unit commitment (SCUC). The information-gap decision theory (IGDT)-based robust model is used for accurate modeling of wind power uncertainty. The characteristics of the proposed IGDT-based robust SCUC model and its benefits are investigated through several technical case studies conducted on the modified 6-bus and 24-bus test systems. The simulation results validate the effectiveness and feasibility of the proposed structure. According to the obtained results for the 6-bus test system, networked energy hubs can help the system operator to reduce the total operating cost, wind power curtailment cost, and C O 2 emission cost by 16.62%, 100%, and 30.44%, respectively, through utilizing up-to-date energy conversion facilities and energy storage systems as well as managing energy demands. It can be seen that the proposed strategy is a very effective step towards achieving a 100% renewable energy system. [ABSTRACT FROM AUTHOR]