In this paper, the possibility of installing small hydraulic turbines in existing water-supply networks, which exploit the daily pressure fluctuations in order to produce energy, is examined. For this purpose, a network of five pressure sensors is developed, which is connected to an artificial intelligence system in order to predict the daily pressure values of all nodes of the network. The sensors are placed at the critical nodes of the network. The locations of the critical nodes are implemented by applying graph theory algorithms to the water distribution network. EPANET software is used to generate the artificial intelligence training data with an appropriate external call from a Python script. Then, an improvement model is implemented using the Harmony Search Algorithm in order to calculate the daily pressure program, which can be allocated to the turbines and, consequently, the maximum energy production. The proposed methodology is applied to a benchmark water supply network and... [more]

This paper deals with the optimal sizing of islanded microgrids (MGs), which use diesel generators to supply energy in off-grid areas. The MG under study integrates photovoltaic (PV) and diesel generation, a battery energy storage System (BESS), and an inverter for the connection between AC and DC voltage buses. Levelised cost of energy (LCOE) and annual system cost (ASC) are considered economic indicators, while the loss of power supply probability (LPSP) is used as a reliability indicator. Fiscal incentives such as the tax benefits and accelerated depreciation applied in Colombia are considered for the optimally sizing of each MG element. Solar measurements were taken at a weather station located in the main campus of Universidad de Antioquia in Medellin, Colombia at a latitude of 6.10 and longitude of −75.38. The objective function is the minimization of the total energy delivered from the power sources that successfully meets the load. The model was implemented in Python programmin... [more]

The Feasible Operating Region (FOR) is defined as a set of points in the PQ plane that includes all the feasible active and reactive power flows at the Transmission System Operator (TSO)−Distribution System Operator (DSO) interconnection. Recent trends in power systems worldwide increase the need of cooperation between the TSO and the DSO for flexibility provision. In the current landscape, the efficient and accurate estimation of the FOR could unlock the potential of the DSO to provide flexibility to the TSO. To that end, much existing research has tackled the problem of FOR estimation, which is a challenging problem. However, no research that adequately organizes the literature exists. This work aims to fill this gap. Three categories of FOR estimation methods were identified: Geometric, Random Sampling, and Optimization-Based methods. The basic principles behind each method are analyzed and the most significant works involving each method are presented. For the reviewed works, we fo... [more]

In this study, we present a Direct Model Reference Adaptive Control (DMRAC) algorithm in a boost converter used in islanded microgrids (MG) with a solar photovoltaic (PV) system. Islanded types of microgrids have very sensitive voltage and frequency variability; therefore, a robust and adaptive controller is always desired to control such variations within the MG. A DC−DC boost converter with a modified MIT rule controller is proposed in this paper, which stabilizes output voltage variations in islanded MG. Since the boost converter is a non-minimum phase, the controller design that relies only on output voltage feedback becomes challenging. Even though output voltage control can be achieved using inductor current control, such current mode controllers may also require prior knowledge of the load resistance and more states, such as output and inductor currents in feedback. Here, two control loops are used to achieve a stable output voltage; a PID controller can regulate the output volt... [more]

Bipolar DC microgrids (BDCMGs) have several issues related to the voltage and require numerous converters to supply power to both poles. To solve these issues, a bidirectional dual-input dual-output (DIDO) converter is proposed for the voltage balancer in BDCMG. The DIDO converter has dual-input sources and a dual-output port connected to the grid. Additionally, the DIDO converter simultaneously performs independent bidirectional power control and voltage balancing control. Based on the input voltages, this paper proposes modulation methods for three cases. The modulation method of the second case has a wide operating range and low balancing current ripple without increasing the switching frequency. Moreover, only voltage balancer mode without active input sources is proposed, considering the intermittent source. Therefore, it can operate as a voltage balancer under all conditions. The voltage balancing performance of the three cases was analyzed. Finally, the proposed modulation and c... [more]

In this paper, a full-order terminal sliding-mode control method is proposed for the rectifier side and the inverter side of the soft open point (SOP). The rectifier-side DC voltage control system consists of the voltage- and current-loops with controllers which are designed using full-order sliding-mode (FOSM) to enhance the dynamic performances and anti-disturbance. The integral type virtual control signal without chattering is designed to compensate for the unmatched uncertainties including external disturbances and some parameter perturbations. The full-order terminal sliding-mode (FOTSM) controller for the current-loop can force the current response to track its reference in finite time. The inverter side power control system is designed to regulate the power. The FOTSM controller for the power-loop ensures the power-tracking accuracy under a disturbed condition. Finally, the simulations demonstrate the effectiveness of the proposed controllers for the rectifier and inverter sides... [more]

One contributor’s name was missing in the original version of the authorship of the paper [...]

Making decisions concerning the use of energy sources is a very difficult and complicated task [...]

Today’s wearable electronics have dramatically altered our daily lives and created an urgent demand for new and intelligent sensor technologies. As a new energy source, self-powering sensors are currently seen as critically important units for wearable and non-wearable textile−electronic systems. To this aim, this paper presents a smart textile-based piezoelectric energy-autonomous harvester and a self-powered sensor for wearable application, where the sandwich structure of the wearable sensor consists of top and bottom textile conductors, and in between the two textile electrodes there is a piezoelectric PVDF thin film. The generating voltage, current, charge, power, and capacitor charging−discharging behaviour of the device were confirmed using multimeter, oscilloscope, Keithley, etc., analyses. Finally, a piezoelectric-textile sensor was integrated into wearable clothes for breathing detection; a shoe insole for footstep recognition; and it can store energy by tapping, to power elec... [more]

The article presents the susceptibility coefficients active power kp and reactive power kq, as proposed by the author. These coefficients reflect the reaction of arc furnaces (change of the furnace operating point) to supply voltage fluctuations. The considerations were based on the model of the arc device in which the electric arc was replaced with a voltage source with an amplitude dependent on the length of the arc. In the case of voltage fluctuations, such a model gives an assessment of the arc device’s behavior closer to reality than the model used, based on replacing the arc with resistance. An example of the application of the kp and kq coefficients in a practical solution is presented.

Recognizing the challenges that today’s hydroponics industry and tomorrow’s plant factories are and will be dealing with requires some strategic thinking [...]

Natural disasters have great destructive power and can potentially wipe out great lengths of power lines. A resilient grid can recover from adverse conditions and restore service quickly. Therefore, the present work proposes a novel methodology to reconfigure power grids through graph theory after an extreme event. The least-cost solution through a minimum spanning tree (MST) with a radial topology that connects all grid users is identified. To this end, the authors have developed an iterative minimum-path heuristic algorithm. The optimal location of transformers and maintenance holes in the grid is obtained with the modified Prim algorithm, and the Greedy algorithm complements the process. The span distance and capacity restrictions define the transformer’s number, where larger spans and capacities reduce the number of components in the grid. The performance of the procedure has been tested in the urban zone Quito Tenis of Ecuador, and the algorithm proved to be scalable. Grid reconfi... [more]

This study aims to provide a comprehensive overview of risk management research developments in the energy sector by using bibliometric analysis techniques. We apply the SciMAT bibliometric analysis software to understand how the intellectual base of this topic has evolved over time and what the major themes are that have contributed to this evolution. We analyse 679 publications referenced in the Web of Science Core Collection and Scopus to map the content of publications on risk management research in the energy sector over a period of 30 years (1993−2022), following the methodical rigour of PRISMA (Preferred Reporting Items for Systematic and Meta-Analyses). Our results identify and support the evolution of risk management research in the energy industry, its interactions, its stability, and changes in its research network. Our work contributes to the current debate on identifying trends and enhancing understanding of the evolution in the energy sector from the perspective of risk m... [more]

With the increasing demand of the power industry for load forecasting, improving the accuracy of power load forecasting has become increasingly important. In this paper, we propose an ultra short-term power load forecasting method based on similar day clustering and EEMD (Ensemble Empirical Mode Decomposition). In detail, the K-means clustering algorithm was utilized to divide the historical data into different clusters. Through EEMD, the load data of each cluster were decomposed into several sub-sequences with different time scales. The LSTNet (Long- and Short-term Time-series Network) was adopted as the load forecasting model for these sub-sequences. The forecast results for different sub-sequences were combined as the expected result. The proposed method predicts the load in the next 4 h with an interval of 15 min. The experimental results show that the proposed method obtains higher prediction accuracy than other comparable forecasting models.

In recent years, algorithmic-based market manipulation in stock and power markets has considerably increased, and it is difficult to identify all such manipulation cases. This causes serious challenges for market regulators. This work highlights and lists various aspects of the monitoring of stock and power markets, using as test cases the regulatory agencies and regulatory policies in diverse regions, including Hong Kong, the United Kingdom, the United States and the European Union. Reported cases of market manipulations in the regions are examined. In order to help establish a relevant digital regulatory system, this work reviews and categorizes the indicators used to monitor the stock and power markets, and provides an in-depth analysis of the relationship between the indicators and market manipulation. This study specifically compiles a set of 10 indicators for detecting manipulation in the stock market, utilizing the perspectives of return rate, liquidity, volatility, market senti... [more]

This research aims to identify (1) whether consumers have an impact on the energy sector, (2) to what extent consumers have an impact on the energy sector, and (3) whether there is so-called energy communism. We understand this phenomenon as the process of energy suppliers imposing energy sources on which the end consumer has zero or very little influence. The research, therefore, focused on a B2C analysis, in five selected countries: the USA, Canada, Australia, the UK, and Poland (N = 500). The research subjects are a homogeneous group in terms of the sources of green energy, and the volume of production of this type of energy and its increments. The investigation was conducted using the procedure appropriate for the triangulation of research methods. Three hypotheses were verified. The first one was rejected, which aimed to determine whether individual consumers are guided by green energy in their choices. The second hypothesis—that energy suppliers do not take into account customer... [more]

The article is devoted to the study of steady-state conditions of a distribution network containing a thyristor voltage regulator. The thyristor voltage regulator (TVR) is a new controlled semiconductor device developed at Nizhny Novgorod State Technical University n.a. R.E. Alekseev (NNSTU). The TVR allows the optimization of the parameters of 6−20 kV distribution networks (currents and voltages) by voltage regulation. An analytical calculation of electromagnetic processes of a distribution network with the TVR has been carried out. The verification of the obtained results has been made using a computer simulation. The dependences of the current and power on additional voltage introduced by the TVR under different voltage regulation modes have been obtained. It has been shown that the use of the TVR enables optimal flow distribution to be ensured over the power transmission lines in proportion to their transfer capability when changing load power and its power factor.

Electricity Price Forecasting (EPF) influences the sale conditions in the energy sector. Proper models of electricity price prognosis can be decisive for choice between energy sources as a start point of transformation toward renewable energy sources. This article aims to present and compare various EPF models scientific publications. Adopted in this study procedure, the EPF publications models are compared into two main categories: the most popular and the most accurate. The adopted method is a bibliometric study as a variation of Systematic Literature Review (SLR) with specified automated queries supported by the VOSviewer bibliometric maps exploration. The subject of this research is the exploration of EPF models in two databases, Web of Science and Scopus, and their content comparison. As a result, the SLR research queries were classified into two groups, the most cited and most accurate models. Queries characteristics were explained, along with the graphical presentation of the re... [more]

Flexible interconnection transformation of a distribution network using all-control power electronics technology will help to overcome the technical bottleneck of distributed generation (DG) penetration and high-quality power supply in the traditional distribution network. However, the flexible interconnection form that uses back-to-back power electronic converters to replace tie switches has disadvantages such as high cost and large footprint, so its mass promotion and application are bound to be limited. Therefore, a new topology of flexible interconnected distribution network with embedded DC system (FDN+EDC) with a better economy is proposed. Firstly, the topology of FDN+EDC is introduced to improve the controllability level of the distribution network under high percentage DG penetration, and is compared with the conventional flexible interconnection method. Secondly, in order to realize the coordinated optimal economic operation of source, network and load in the FDN+EDC, a dynam... [more]

In this paper, a neural network model called Long Short-Term Temporal Convolutional Network (LST-TCN) model is proposed for short-term load forecasting. This model refers to the 1-D fully convolution network, causal convolution, and void convolution structure. In the convolution layer, a residual connection layer is added. Additionally, the model makes use of two networks to extract features from long-term data and periodic short-term data, respectively, and fuses the two features to calculate the final predicted value. Long Short-Term Memory (LSTM) and Temporal Convolutional Network (TCN) are used as comparison algorithms to train and forecast 3 h, 6 h, 12 h, 24 h, and 48 h ahead of daily electricity load together with LST-TCN. Three different performance metrics, including pinball loss, root mean squared error (RMSE), and mean absolute error (RASE), were used to evaluate the performance of the proposed algorithms. The results of the test set proved that LST-TCN has better generalizat... [more]

This paper presents the mathematical model and control of the voltage source inverter (VSI) connected to an alternating current (AC) microgrid. The VSI used in this work was a six-switch three-phase PWM inverter, whose output voltages were controlled in a synchronous (dq) reference frame via a sliding mode control strategy. The control strategy required only output voltages; other states of the system were estimated by using a high-gain observer. The power-sharing among multiple inverters was achieved by solving power flow equations of the electrical network. The stability analysis showed that the error was ultimately bound in the case of the real PWM inverter and/or with a nonlinear load in the electrical network. The microgrid was simulated using the SimPowerSystems Toolbox from MATLAB/Simulink. The simulation results show the effectiveness of the proposed control scheme. The output voltage regulation of the inverter and power-sharing was achieved with the ultimately bounded error fo... [more]

Water is an essential element for life [...]

The loss of distribution networks caused by various electrical motors including transformers and generators can significantly affect the efficiency and economical operation of the power grid, especially for new power systems with high penetration of renewable energies. In this paper, the potential of using an energy storage system (ESS) for loss reduction is investigated, where a novel two-stage method for key-bus selection and ESS scheduling is proposed. At the first stage, the most sensitive key buses to the variation of load are selected by using the loss sensitive factors (LSF) method. At the second stage, ESS scheduling is conducted by solving an optimization problem with uncertainties caused by high penetration of renewable energies, where the uncertainties are characterized by confidence levels. The optimal scheduling of ESS including locations, capacities, and working modes are obtained at the second stage. The effectiveness of the proposed method is demonstrated via numerical... [more]

Microgrids have a lot to offer, including helping smart grids operate on distribution grids or bringing electricity to some cities. The management system receives and transmits different states. This is because the elements adapt to the conditions of the network in the shortest possible time. The 5G communication technology has high transmission speed, owing to which it can improve equipment connectivity and reduce latency, allowing the real-time analysis and monitoring of electrical microgrids considerably better than earlier generations. In addition, it is estimated that, in the near future, many cities will be connected using communication systems that allow the interconnection of different systems safeguarding the connectivity, speed, and response time of these elements in an electrical system, smart grid, or microgrids with the growing development of the Internet of Things. For this reason, it is essential to analyze the integration of 5G technology to improve the management of mi... [more]

The microgrid concept has evolved from the humble origins of simple remote electrification applications in rural environments to complex architectures. Microgrids are key enablers to the integration of higher penetrations of renewables in the energy sector (including electricity, heating, cooling, transport and industry). In addition to the local energy sources, energy storage systems and loads, the modern microgrid encompasses sophisticated energy and power management systems, peer-to-peer energy markets and digital technologies to support this energy transition. The microgrid concept has recently been applied to all energy sectors, in order to develop solutions that address pressing issues related to climate change and the decarbonization of these important sectors. This paper initially reviews novel applications in which the microgrid concept is being applied, from a detailed analysis of recent literature. This consists of a comprehensive analysis of the state of the art in shipboar... [more]