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

The increase in renewable energy sources (RESs) in distribution grids is a major driver for achieving green energy goals worldwide. However, RES power inverters affect power quality, increase power losses, and, in certain cases, may cause power interruptions due to harmonics, deterioration of the rate of change of frequency, and inability to rapidly react in grid faults. Today, phasor measurement units (PMUs) are the ultimate tools for real-time monitoring of distribution grids’ health, and they enable several data-driven added-value services such as fast and automated fault detection, isolation, and recovery; state estimation; power quality monitoring; dynamic events analysis, etc. The present paper proposes an open hardware and software PMU platform, which is low cost, high performance, expandable, and, in general, suitable for research and innovation activities. The system is based on two processor modules (a digital signal processor from Texas Instruments TMS320c5517, and a microprocessor System-in-Package from Octavo Systems OSD3358), two local databases of 64 Gbytes each, GPS module, 5G modem interface, as well as analog and signal conditioning circuits to interface three-phase power voltage and current signals. The entire hardware design, schematics, and instrumentation components, as well as all firmware and software functions are completely open source. Pilot operation of the prototype design has been installed in three medium-/low-voltage substations in Cyprus, as well as twelve substations in Spain and Italy.

/pdf/openedgepmu-an-open-pmu-architecture-with-edge-processing-2wzc1nyu.pdf

OpenEdgePMU: An Open PMU Architecture with Edge Processing for Future Resilient Smart Grids

The increasing penetration of renewable energy sources tends to redirect the power systems community’s interest from the traditional power grid model towards the smart grid framework. During this transition, load forecasting for various time horizons constitutes an essential electric utility task in network planning, operation, and management. This paper presents a novel mixed power-load forecasting scheme for multiple prediction horizons ranging from 15 min to 24 h ahead. The proposed approach makes use of a pool of models trained by several machine-learning methods with different characteristics, namely neural networks, linear regression, support vector regression, random forests, and sparse regression. The final prediction values are calculated using an online decision mechanism based on weighting the individual models according to their past performance. The proposed scheme is evaluated on real electrical load data sensed from a high voltage/medium voltage substation and is shown to be highly effective, as it results in R2 coefficient values ranging from 0.99 to 0.79 for prediction horizons ranging from 15 min to 24 h ahead, respectively. The method is compared to several state-of-the-art machine-learning approaches, as well as a different ensemble method, producing highly competitive results in terms of prediction accuracy.

N. Giamarelos

Papers

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

OpenEdgePMU: An Open PMU Architecture with Edge Processing for Future Resilient Smart Grids

A Machine Learning Model Ensemble for Mixed Power Load Forecasting across Multiple Time Horizons