Probabilistic Planning of Distribution Networks with Optimal DG Placement Under Uncertainties

This research paper presents an efficient methodology for distribution network planning under an uncertain environment. As an extension of our previous work presented at the ECCE Asia 2021 conference, here optimal placement and sizing of Renewable Energy Sources (RES)-based Distributed Generations (DGs) are determined considering the generation and load uncertainties. In addition, the optimal tap settings of off-load tap changing transformers present in a network are also determined. Probabilistic non-linear optimization is solved with a sensitivity-based technique to minimize the distribution network losses and improve its voltage stability. The proposed methodology is implemented on standard test systems like the IEEE 69 bus and the Indian 85 bus networks. Further, to determine its real-world functionality, the methodology is tested on a practical radial distribution network of 88 buses present in a remote Froan island of Norway. When compared with existing techniques, the proposed methodology provides much more efficient network planning solutions with lesser power losses. Developed on free and open-source software platforms, it also provides a reliable and cost-effective alternative to network operators to determine their network robustness.acceptedVersio

Stochastic operation of energy constrained microgrids considering battery degradation

Power systems with high penetration of variable renewable generation are vulnerable to periods with low generation. An alternative to retain high dispatchable generation capacity is electric energy storage that enables utilization of surplus power, where the electric energy storage contributes to the security of supply. Such systems can be considered as energy-constrained, and the operation of the electric energy storage must balance the minimization of the current operating costs against the risk of not being able to meet the future demand. Safe and efficient operation requires stochastic methods with sufficient foresight. Operation dependent storage degradation is a complicating factor. This paper proposes a linear approximation of battery state-of-charge degradation and implements it in a stochastic dual dynamic programming based energy-management model in combination with cycling degradation. The long-term implications of degradation modeling in the daily operation are studied for a small Norwegian microgrid with variable renewable power generation and limited dispatchable generation capacity as well as battery and hydrogen storage to balance supply and demand. Our results show that the proposed strategy can prolong the expected battery lifetime by more than four years compared to the naive stochastic strategy but may cause increased degradation for other system resources. © 2022 The AuthorsStochastic operation of energy constrained microgrids considering battery degradationpublishedVersio

RELSAD: A Python package for reliability assessment of modern distribution systems

publishedVersio

An overview on formulations and optimization methods for the unit-based short-term hydro scheduling problem

The short-term hydro scheduling (STHS) problem aims at determining the optimal power generation schedules for either a single hydropower plant or an integrated system of cascaded watercourses during a time horizon from a single day to one week. Traditionally, an aggregated plant concept is usually adopted in the formulation of the STHS problem. The hydro-turbine generator units in a plant are aggregated as one equivalent unit. Nowadays, more and more hydro producers participate in both energy and capacity markets. It highlights the need for the precise calculation for energy conversion and available capacity of each unit. Formulating the STHS problem on individual units can accurately capture the physical and the operational characteristics of the unit. In this overview, a detailed classification of mathematical programming approaches to model and solve the unit-based STHS problem is presented. The various modeling techniques proposed in the publications since 2000 are categorized by their objectives and constraints. This provides a comprehensive comparison and discussion for each specific issue in the formulation of STHS. We anticipate this overview to be a starting point for finding more computationally solvable and effective methods to handle the challenges in the unit-based STHS problem. © 2019 Elsevier B.V.An overview on formulations and optimization methods for the unit-based short-term hydro scheduling problempublishedVersio

Frequency support by wave farms in low inertia power systems

New ancillary services and additional requirements for the grid integration of variable renewable energy (VRE) are being defined worldwide, in response to the technical challenges caused by increasing levels of VRE utilization in electric power grids. Currently, the use of wave energy is still limited to a few applications or demonstrations, where the level of penetration of wave power into the grid is not significant. To anticipate the future requirements for wave power integration, and the possibilities for provision of services, this paper considers the lessons being learned through the challenges caused by high penetration levels of other VRE sources into the grid, particularly wind power. On this basis, this paper presents an overview of grid support services that wave power plants can be expected to provide in power systems dominated by converter-interfaced generation, i.e. low inertia systems. Specifically, the focus is on services that support the active power balance in the power system. Then, the current capabilities and future perspectives for the provision of frequency support by wave farms are discussed

Implementing Hydropower Scheduling in a European Expansion Planning Model

A method for implementing an enhanced hydropower planning formulation in a long-term expansion planning model is proposed. The methodological framework involves assigning hydropower generation a marginal cost through water values, enabling comparability with the marginal costs of competitive technologies. Added robustness and details in the representation of hydropower and its inherent storage capabilities allows for a more precise evaluation of the technology's impact on optimal investments for other power resources. The impact for intermittent renewable energy sources such as wind and solar power is especially interesting to analyze. Examination of effects from the richer formulation is carried out for an EU 20-20-20 like policy scenario. Optimization results for Europe in the period 2010 to 2060 show that the new framework leads to decreased utilization of hydropower due to its more precise valuation through water values, as well as lower inflow for run-of-the-river hydropower than previously. Therefore, additional investments are carried out for other energy sources that are deemed more economically beneficial. Notably, an earlier deployment of solar power is part of the revamped investment scheme

EMD Mode Mixing Separation of Signals with Close Spectral Proximity in Smart Grids

The Empirical Mode Decomposition (EMD) is a signal analysis method that separates multi-component signals into single oscillatory modes called intrinsic mode functions (IMFs). These IMFs can generally be associated to a physical meaning of the process from which the signal is obtained. When the phenomena of mode mixing occurs, as a result of the EMD sifting process, the IMFs may lose their physical meaning hindering the interpretation of the results of the analysis. Previous research presents a rigorous mathematical analysis that shows how EMD behaves in the case of a composite two-components signal, explaining the roots of the mode mixing problem. Also, the frequency-amplitude region within which a good separation is achieved with EMD is well identified and discussed. However, a solution that offers good IMF separation when components reside within the same octave is not yet available. In this paper, a method to separate spectral components that reside within the same octave, is presented. This method is based on reversing the conditions by which mode mixing occurs presented in the paper "One or Two frequencies? The Empirical Mode Decomposition Answers", in [3]. Numerical experiments with signals containing spectral components within the same octave shows the effective separation of modes that EMD can perform after this principle is applied. This separation technique has potential application for identifying the cause of different oscillatory modes with spectral proximity present in the smart grid

Switching sequences for non-predictive declutching control of wave energy converters

Aiming at improving the energy absorption from waves, a number of studies have considered declutching control - a phase-control method that consists of disengaging the power take-off (PTO) system from the oscillating body at specific intervals of time. The on/off sequences with the instants to engage/disengage the PTO are usually determined by optimization procedures that require the knowledge of future excitation force, which remains an open challenge for practical implementation. This paper presents a comprehensive numerical study with different PTO damping coefficients for declutching control. It is shown that the value of the damping plays an important role on the efficacy of the control method and on the optimal time to engage (or disengage) the PTO. Then, two switching sequences that use current information of the body motion are proposed, and compared with the threshold unlatching strategy. When the body velocity vanishes, the PTO is clutched (declutched) if the current estimation of the mean excitation force frequency is lower (higher) than the body resonant frequency. The instant to declutch (clutch) again depends on the damping coefficient. The resultant PTO force profiles are not optimal, but act in an effective way to improve the energy absorption, while not requiring wave short-term predictions and numerical optimization solutions that can be time-consuming depending on the fidelity of numerical models and the prediction horizon. Numerical simulations consider real ocean waves and synthetic waves. Copyright © 2020 The Authors. This is an open access article under the CC BY-NC-ND licensepublishedVersio