A Survey of Digital Twin Techniques in Smart Manufacturing and Management of Energy Applications

An advanced Grey Wolf Optimization Algorithm and its application to planning problem in smart grids

Trajectory planning of autonomous mobile robots applying a particle swarm optimization algorithm with peaks of diversity

Optimal reactive power dispatch (ORPD) with integration of renewable energy resources has a growing interest in research community due to its utmost requirements during the operation, planning and design of the modern electrical power networks. The objective of ORPD is to improve the performance of power network by means of reducing the losses in transmission line, improving the voltage profile, and decreasing the overall cost of operation through optimal tuning of the operational variables such as tap position of transformers, generator output voltages and capacitor banks. However, the nonlinear, non-stationary and complex nature of power network, presence of load uncertainties, and dynamic behavior of wind generation introduces a complex optimization task which cannot be readily solved in an efficient manner. In this research work, a new fractional memetic computing paradigm, i.e., the fractional particle swarm optimization gravitational search algorithm with entropy evolution (FPSOGSA-EE), is designed to solve the ORPD problems in power system adopting wind power plants (WPPs) and load uncertainties. The proposed optimization framework FPSOGSA-EE integrates the concept of fractional calculus and Shannon entropy to strengthen the optimization characteristics of canonical algorithm. The exhaustive experimentation endorse the efficacy of FPSOGSA-EE by providing minimum gauge of fitness evaluation function, namely, the line loss and voltage deviation index minimization, in IEEE 30 and 57 bus networks. The stability, consistency and reliability of proposed FPSOGSA-EE is ascertained through statistical interpretations by means of boxplots, probability measures for cumulative distribution function, and histogram illustrations. 

Fractional memetic computing paradigm for reactive power management involving wind-load chaos and uncertainties

Designing a reliable and robust micro-grid (MG) aided by energy storage devices requires quantifying the parametric uncertainty associated with input data time-series, among other types of uncertainties – and in particular, the uncertainty in forecasted meteorological, load demand, and wholesale electricity price time-series. Given the computational complexities involved, the recent battery-storage-supported MG capacity optimisation literature fails to simultaneously characterise multiple sources of data uncertainty. This paper, therefore, seeks to address this knowledge gap by hedging against a wide array of parametric uncertainties at the same time, whilst exploring the potentially salient implications of high-dimensional uncertainty characterisation for the costing and configuration of battery-supported, grid-tied MGs. To this end, this paper introduces a novel computationally efficient, stochastic MG design optimisation model that enables the simultaneous handling of multiple uncertain inputs. Notably, the model yields in-depth, accurate, and robust infrastructure decision-making support, particularly with regard to the optimal size of battery storage, by specifically analysing the worst-case, most likely case, and best-case uncertainty characterisation scenarios. To demonstrate the effectiveness of the model within a community renewable energy project scheme, a case study of a grid-connected, battery-storage-supported MG in the town of Ohakune, New Zealand is presented. Importantly, the numerical simulation results indicate that the life-cycle cost of the MG would have been underestimated by as much as ~8% (equating to NZ$0.3 m) in the most likely case – with an associated ~15% (equating to 143 kWh) battery size underestimation – if the variability inherent in forecast inputs was not factored into the analysis. Also, the proportion of the energy storage capacity to the total renewable power generation capacity is ~71%, ~76%, and ~68% in the best-case, most likely case, and worst-case scenarios, respectively, suggesting that the risk-seeking and risk-averse MG planning strategies have a slightly contradictory effect on the storage-to-generation ratio. Comprehensive sensitivity analyses of different battery technologies with different specific power, specific energy, and depth-of-discharge specifications have also demonstrated the robustness and validity of the model, whilst additionally identifying the sodium‑sulfur (NaS) battery technology as the most profitable choice in grid-connected MG development applications – which can be primarily attributed to the fact that it provides the best trade-off between the specific power and specific energy. 

https://figshare.com/articles/journal_contribution/Quantifying_the_effects_of_forecast_uncertainty_on_the_role_of_different_battery_technologies_in_grid-connected_solar_photovoltaic_wind_micro-hydro_micro-grids_An_optimal_planning_study/22045397/1/files/39137696.pdf

Quantifying the effects of forecast uncertainty on the role of different battery technologies in grid-connected solar photovoltaic/wind/micro-hydro micro-grids: An optimal planning study

A multitude of optimization tasks ensue in the context of the smart grid, often exhibiting undesirable characteristics like non-convexity, mixed types of design variables and multiple - and often conflicting - objectives. These tasks can be broadly categorized into three classes of problems, namely optimal power flow (OPF), scheduling and planning. Metaheuristic search methods form a generic class of optimization techniques, that have been shown to work successfully for complex problems. Not surprisingly, they have been widely applied in the smart grid, their use spanning almost every smart grid-related optimization task. In this work, we review the use of metaheuristic search for OPF, scheduling and planning through a unified approach, keeping in mind that these problems share many common challenges and objectives. The use of different metaheuristic methods is discussed extensively with regard to problem handling, multi-objective optimization performance and method accuracy in relation to computational complexity. An attempt to arrive at quantitative conclusions is also being made, by compiling tables which present collective results on common test grids. Lastly, the paper identifies promising directions for future research, concerning metaheuristic search application practices, method development and new challenges that we believe will shape the future of smart grid optimization.

Metaheuristic search in smart grid: A review with emphasis on planning, scheduling and power flow optimization applications

Short-term load forecasting is a key task for planning and stability of the current and future distribution grid, as it can significantly contribute to the management of energy market for ancillary services. In this paper we introduce the beneficial properties of applications of sparse representation and corresponding dictionary learning to the net load forecasting problem on a substation level. In this context, sparse representation theory can provide parsimonial predictive models, which become attractive mainly due to their ability to successfully model the input space in a self-learning manner, by interacting between theory, algorithms, and applications. Several techniques are implemented, incorporating numerous dictionary learning and sparse decomposition algorithms, and a hierarchical structured model is proposed. The concept of sparsity in each case is embedded throughout the utilization of different regularization forms which include the $\ell _{0} $ , $\ell _{1} $ , $\ell _{2} $ and $\ell _{0}^{tree} $ norms. The observed superiority of the proposed theory, especially the one which embeds the atoms and corresponding coefficients in a tree structure, stems from the construction of the dictionary so as to represent efficiently the ambient electricity signal space and the consequent extraction of sparse basis-vectors. The performance of each model is evaluated using real hourly load measurements from a high voltage/medium voltage (HV/MV) substation and compared with that of widely used machine learning methods. The provided analytical results, verify the effectiveness of hierarchical sparse representation in short-term load forecasting applications, in terms of common accuracy indices.

/pdf/short-term-electric-load-forecasting-with-sparse-coding-55i6zolrfb.pdf

Short-Term Electric Load Forecasting With Sparse Coding Methods

The field of automatic collision avoidance for surface vessels has been an active field of research in recent years, aiming for the decision support of officers in conventional vessels, or for the creation of autonomous vessel controllers. In this paper, the multi-ship control problem is addressed using a model predictive controller (MPC) that makes use of obstacle ship trajectory prediction models built on the RBF framework and is trained on real AIS data sourced from an open-source database. The usage of such sophisticated trajectory prediction models enables the controller to correctly infer the existence of a collision risk and apply evasive control actions in a timely manner, thus accounting for the slow dynamics of a large vessel, such as container ships, and enhancing the cooperation between controlled vessels. The proposed method is evaluated on a real-life case from the Miami port area, and its generated trajectories are assessed in terms of safety, economy, and COLREG compliance by comparison with an identical MPC controller utilizing straight-line predictions for the obstacle vessel.

Multi-Ship Control and Collision Avoidance Using MPC and RBF-Based Trajectory Predictions.

This work presents a novel data-driven modeling approach for the direct prediction of a vessel’s trajectory through the use of AIS data. The proposed method is based on radial basis function neural networks trained with the fuzzy means algorithm, a combination which produces models of high accuracy and simple structures. The produced model is applied on real AIS data in order to approximate the behavioral patterns of cargo ships when moving in the vicinity of a busy port. Results show that the proposed method outperforms a well-established machine learning technique, namely multi-layer perceptrons, not only in terms of accuracy for one-step and multi-step-ahead prediction, but also by providing lower computational times; these facts make it suitable for use in receding horizon integrated control frameworks.

Myron Papadimitrakis

Papers

Metaheuristic search in smart grid: A review with emphasis on planning, scheduling and power flow optimization applications

Short-Term Electric Load Forecasting With Sparse Coding Methods

Multi-Ship Control and Collision Avoidance Using MPC and RBF-Based Trajectory Predictions.

Vessel Trajectory Prediction Using Radial Basis Function Neural Networks