The electrical energy industry is undergoing dramatic changes; the massive deployment of renewables, an increasing share of DC networks at transmission and distribution levels, and at the same time, a continuing reduction in conventional synchronous generation, all contribute to a situation where a variety of technical and economic challenges emerge [...]

The single pole-to-ground (SPG) fault is one of critical failures which will have a serious impact on the stable operation of the multi-terminal DC distribution network based on the modular multilevel converter (MMC). It is very significant to analyze fault characteristics for detecting faults and protection design. This paper established the DC SPG fault model, which showed that in the presence of a reactor, the short-circuit current was reduced from 2.3 kA to 1 kA at 6 ms after the fault. Then, a novel SPG fault protection strategy was proposed, which detected the current derivative in connection transformer grounding branch. When the value increases past the threshold of current derivative, small resistance was switched on to increase fault current. Thus, the reliability of differential protection was enhanced. Compared with the traditional protection method, the proposed method does not need communication, and improved the speed of protection. Finally, the simulation model was esta... [more]

This study focuses on the important issue of predicting electricity load for efficient energy management. To achieve this goal, different statistical methods were compared, and results over time were analyzed using various ratios and layers for training and testing. This study uses an artificial neural network (ANN) model with advanced prediction techniques such as genetic algorithms (GA) and adaptive neuro-fuzzy inference systems (ANFIS). This article stands out with a comprehensive compilation of many features and methodologies previously presented in other studies. This study uses a long-term pattern in the prediction process and achieves the lowest relative error values by using hourly divided annual data for testing and training. Data samples were applied to different algorithms, and we examined their effects on load predictions to understand the relationship between various factors and electrical load. This study shows that the ANN−GA model has good accuracy and low error rates f... [more]

Given the constraints associated with grid expansion costs, limited access to reliable electricity, and priorities in addressing the climate agenda and Sustainable Development Goals in low-income countries, microgrids and off-grid solar projects represent a viable solution for rural electrification. This type of solution has the advantage of being less expensive than conventional technologies, is rapidly scalable, affordable, environmentally sustainable, and can play a critical role in empowering rural communities. In this context, this study proposed a spatial framework for off-grid solar energy planning based on a Geographical Information System and Boolean logic, Fuzzy logic, and Analytic Hierarchy Process Multicriteria Decision-Making methods. The results of the applied methodology show that the selection of optimal locations for off-grid solar photovoltaic microgrid projects in Mozambique is significantly influenced by the following order of criteria: climatology, orography, techn... [more]

One of the challenges faced by Brazilian distribution utilities to enable the connection and operation of microgrids (MGs) is the absence of a solid set of technical standards in the country. An alternative has been to use and adapt existing standards applied to micro- and mini-distributed generation. In this context, this paper presents an analysis of the development status of norms, standards, and general requirements for the connection and operation of microgrids, as well as a proposal for the regulation and structuring of technical and operational requirements related to the implementation of microgrid projects. Some critical points highlighted in the paper include: the modes of operation, the minimum requirements for the different modes of operation, interoperability of systems, a conceptual model with attribution of responsible actors for the decentralized management of microgrids adapted to the institutional standards of the Brazilian sectorial model, a proposal for a standard c... [more]

Due to the intermittent and variable nature of wind, Wind Power Generation Forecast (WPGF) has become an essential task for power system operators who are looking for reliable wind penetration into the electric grid. Since there is a need to forecast wind power generation accurately, the main contribution of this paper is the development, implementation, and comparison of WPGF methods in a framework to be used by distribution system operators (DSOs). The methodology applied comprised five stages: pre-processing, feature selection, forecasting models, post-processing, and validation, using the historical wind power generation data (measured at secondary substations) of 20 wind farms connected to the medium voltage (MV) distribution network in Portugal. After comparing the accuracy of eight different models in terms of their relative root mean square error (RRMSE), extreme gradient boosting (XGBOOST) appeared as the best-suited forecasting method for wind power generation. The best avera... [more]

Accurate short-term load forecasting is of great significance to the safe and stable operation of power systems and the development of the power market. Most existing studies apply deep learning models to make predictions considering only one feature or temporal relationship in load time series. Therefore, to obtain an accurate and reliable prediction result, a hybrid prediction model combining a dual-stage attention mechanism (DA), crisscross grey wolf optimizer (CS-GWO) and bidirectional gated recurrent unit (BiGRU) is proposed in this paper. DA is introduced on the input side of the model to improve the sensitivity of the model to key features and information at key time points simultaneously. CS-GWO is formed by combining the horizontal and vertical crossover operators, to enhance the global search ability and the diversity of the population of GWO. Meanwhile, BiGRU is optimized by CS-GWO to accelerate the convergence of the model. Finally, a collected load dataset, four evaluation... [more]

Incorporating photovoltaic (PV) inverters in power distribution systems via static synchronous compensators (PV-STATCOM) during the nighttime has lately been described as a solution to improve network performance. Hunter prey optimization (HPO) is introduced in this study for efficient PV-STATCOM device allocation in distribution systems. HPO generates numerous scenarios for how animals could act when hunting, some of which have been expanded into stochastic optimization. The PV-STATCOM device allocation issue in distribution networks is structured to simultaneously minimize the electrical energy losses and improve the voltage profile while accounting for variable 24 h loadings. The impacts of varying the number of installed PV-STATCOM devices are investigated in distribution systems. It is tested on two IEEE 33-node and 69-node distribution networks. The effectiveness of the proposed HPO is demonstrated in comparison to the differential evolution (DE) algorithm, particle swarm optimiz... [more]

The advent of distributed renewable energy sources (DRESs) has led to the progressive transformation of traditional distribution networks to active components of the power system. This transformation, however, may jeopardize the reliable grid operation due to the advent of new technical problems, such as network overloading, over-/under-voltage events, abnormal frequency deviation and dynamic instability. In this challenging scenery, the installation of a modern measuring infrastructure has created new sources of data and information that facilitate the provision of ancillary services (ASs) via measurement-based analysis. The ACTIVATE (ancillary services in active distribution networks based on monitoring and control techniques) project aims to design innovative AS solutions for power system operators. These solutions aim to tackle the technical issues emerged by the ever-increasing DRES penetration and their volatile nature. In this context, in ACTIVATE, a holistic system is proposed... [more]

The integration of distributed energy resources, such as wind farms (WFs) and energy storage systems (ESSs), into distribution networks can lower the economic cost of power generation. However, it is essential to consider operational constraints, including loading margin, which ensures the security line contingency. This study aims to develop a comprehensive hourly distribution network reconfiguration (HDNR) model to minimize the economic cost for the power generation company. The model considers the optimal allocation of WFs and ESSs in terms of capacity and location, as well as the hourly status of the distribution network switches, based on security constraints. The proposed model is applied to an IEEE 33-bus distribution test system, and the capacities and locations of WFs and ESSs are determined. The impacts of security constraints on the optimal capacities and locations of WFs and ESSs, and the hourly configuration of the distribution network, are analyzed based on two case studi... [more]

Due to the uncontrollable generators, islanded microgrids powered only by renewable energy require costly energy storage systems. Energy storage needs are amplified when load and generation are misaligned on hourly, monthly, or seasonal timescales. Diversification of both loads and generation can smooth out such mismatches. However, the ideal type of battery to smooth out remaining generation deficits will depend on the duration(s) that energy is stored. This study presents a controls co-design approach to design an islanded microgrid, showing the benefit of hybridizing tidal and solar generation and hybridizing lithium-ion and flow battery energy storage. The optimization of the microgrid’s levelized cost of energy is initially studied in grid-search slices to understand convexity and smoothness. Then, a particle swarm optimization is proposed and used to study the sensitivity of the hybrid system configuration to variations in component costs. The study highlights the benefits of con... [more]

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 micropr... [more]

The integration of distributed generation (DG) into a power distribution network allows the establishment of a microgrid (MG) system when the main grid experiences a malfunction or is undergoing maintenance. In this case, the power-generating capacity of distributed generators may be less than the load demand. This study presents a strategy for the effective utilization of deployed active and reactive power sources under power mismatch conditions in the islanded distribution networks. Initially, the DGs’ and capacitors’ optimal placement and capacity were identified using the Jaya algorithm (JA) with the aim to reduce power losses in the grid-connected mode. Later, the DG and capacitor combination’s optimal power factor was determined to withstand the islanded distribution network’s highest possible power demand in the event of a power mismatch. To assess the optimal value of the DG−capacitor pair’s operating power factor (pfsource) for the islanded operation, an analytical approach ha... [more]

The energy crisis caused by global structural changes in the economic sphere is the cause accelerating the energy transition based on the concept of sustainable development. This study is to test the hypothesis about the incentive effect of tax expenditures on alternative energy and energy conservation. The objects of empirical research are the EU, OECD countries, OECD partner countries and Russia from 2018−2020. The tools of scientific research are based on methods of economic−statistical and comparative analysis and expert judgments. The concept of tax expenditures in terms of decarbonization is analyzed using a systematic approach. The integrated methodological approach shows the relationship between the tax policy and government strategies in achieving sustainable development goals to ensure the transition to rational energy consumption patterns and sustainable energy sources. The authors analyze incentives for the energy sector and alternative energy sources in the considered grou... [more]

The paper considers the issues of maintaining an equality of flow in generated and consumed electric energy in an electric network incorporating an electric power storage system. An analysis of ways to equalize the energy and power balance was carried out, and the advantages of using electricity storage systems in electrical networks was assessed. Upon simulation using the Power Factory program, we noted that, after switching on the load, a transient process occurs, characterized by a jump in active power, which was caused by the need for time to initiate the electric energy storage system. However, immediately after this, the process of issuing the accumulated energy to the electrical network and compensating for energy consumption began. Moreover, when the load was disconnected, there is a certain dip in the active power curve and a further increase in consumption. This was found to be due to the transition of the electricity storage system to the modes of energy storage and battery... [more]

This article (case study) discusses the influence of household photovoltaic generation on the voltage quality in a three-phase 0.4 kV grid. The research analyzes remotely acquired data from two specially designed three-phase Y-connected power meters to understand the PV system’s influence on the grid. The values of the voltages, currents, THD, individual harmonics, power factor, and K-factor are obtained every 200 ms over several months, creating more than 20 GB of data. The loads are typical household electrical appliances and EV portable chargers. The main conclusion after analyzing the measurements is that three-phase PV generation in the presence of household loads can create or increase grid unbalance; important current values into the neutral wire and THD and K-factor increase in some cases. The results also uncovered that without current sensing at the household connection point to the three-phase grid to control the PV inverter, the balanced phase output power of the grid-tied... [more]

The highly variable power generated from a battery energy storage system (BESS)−photovoltaic distributed generation (PVDG) causes harmonic distortions in distribution systems (DSs) due to the intermittent nature of solar energy and high voltage rises or falls in the BESS. Harmonic distortions are major concerns in the DS, especially when the sizes and locations of these resources are sub-optimal. As a result, many studies are being performed on the optimal allocation of BESS/PVDG systems in distribution network systems. In this regard, this paper seeks to review the existing planning models, optimization methods and renewable energy resources that uncertainty models have employed in solving BESS/PVDGs allocation problems in terms of obtaining optimal solutions/allocations and curtailing the harmonic contents of the DSs. However, studies on optimal allocation planning of BESS/PVDGs have achieved minimum cost but were not able to meet the standard harmonic level of the DSs. The results i... [more]

The concept of peer-to-peer (P2P) energy trading leads to the flexible energy transaction of prosumers and consumers, for which the P2P business model is normally the main attention. It still requires system operators to address the challenges in trading and constraint problems. In this context, this work regards the congestion constraint in conjunction with energy trading. Firstly, a matching approach based on the cost path is proposed. It is consistent with the cost for the dispatch along each route, making a suitable matching in both distance and bids. In combination with the matching process, the available capacity has to be considered to avoid line congestion. Secondly, the bus transfer factor (BTF) and the partitioning zone approach are proposed to overcome the issue. BTF refers to a response of bus power to the congested line power. The partitioning zone, separated into the source and the load area, enables a simple management strategy. Thereby, the power adjustment in each area... [more]

In order to meet the demands of desirable efficiency, transformerless DC/DC equipment with great voltage step-down are inevitable needed. This research offers a unique type of high-frequency, high-voltage-gain DC/DC converter, which comprises a switched capacitor (SC) converter and a buck converter. Thanks to the transformation of a two-stage converter to a single-stage converter, it has a considerable ratio of step-down voltage transformation and a reasonable duty cycle. In addition, it can permit low voltage stress on the switches. The simple control method and easy driving circuit implementation makes it scalable for high-power-level devices. Low cost can be realized as fewer components are needed. Under all operational circumstances, total soft-charging and low equipment voltage stresses are accomplished. Compared to those classic high-voltage-gain converters, the proposed converter exhibits merits of higher efficiency, higher flexibility, lower ripples, and lower costs. A comprehe... [more]

Accurate multivariate load forecasting plays an important role in the planning management and safe operation of integrated energy systems. In order to simultaneously reduce the prediction bias and variance, a hybrid ensemble learning method for load forecasting of an integrated energy system combining sequential ensemble learning and parallel ensemble learning is proposed. Firstly, the load correlation and the maximum information coefficient (MIC) are used for feature selection. Then the base learner uses the Boost algorithm of sequential ensemble learning and uses the Bagging algorithm of parallel ensemble learning for hybrid ensemble learning prediction. The grid search algorithm (GS) performs hyper-parameter optimization of hybrid ensemble learning. The comparative analysis of the example verification shows that compared with different types of single ensemble learning, hybrid ensemble learning can better balance the bias and variance and accurately predict multiple loads such as el... [more]

This paper aims to improve the forecasting of electricity market prices by incorporating the characteristics of electricity market prices that are discretely affected by the fuel cost per unit, the unit generation cost of the large-scale generators, and the demand. In this paper, two new techniques are introduced. The first technique applies feature generation to the label and forecasts the transformed new variables, which are then post-processed by inverse transformation, considering the characteristic of the fuel types of marginal generators or prices through two variables: fuel cost per unit by the representative fuel type and argument of the maximum of Probability Density Function (PDF) calculated by Kernel Density Estimation (KDE) from the previous price. The second technique applies decomposition to the demand, followed by a feature selection process to apply the major decomposed feature. It is verified using gain or SHapley Additive exPlanations (SHAP) value in the feature selec... [more]

Solar radiation has important impacts on buildings such as for cooling/heating load forecasting, energy consumption forecasting, and multi-energy complementary optimization. Two types of solar radiation data are commonly used in buildings: radiation data in typical meteorological years and measured radiation data from meteorological stations, both of which are types of historical data. However, it is difficult to predict the hourly global solar radiation, which affects the application of relevant prediction models in practical engineering. Most existing methods for predicting hourly global solar radiation have issues such as difficulty in obtaining input parameters or complex data processing, which limits their practical engineering applications. This study proposed a simplified method to accurately predict the hourly horizontal solar radiation using extraterrestrial solar radiation, weather types, cloud cover, air temperature, relative humidity, and time as the input parameters. The b... [more]

This paper presents an in-depth comparison of the benefits and limitations of using a low-voltage DC (LVDC) microgrid versus an AC microgrid with regard to the integration of low-carbon technologies. To this end, a novel approach for charging electric vehicles (EVs) on low-voltage distribution networks by utilizing an LVDC backbone is discussed. The global aim of the conducted study is to investigate the overall energy losses as well as voltage stability problems on DC and AC microgrids. Both architectures are assessed and compared to each other by performing a power flow analysis. Along this line, an actual low-voltage distribution network with various penetration levels of EVs, combined with photovoltaic (PV) systems and battery energy storage systems is considered. Obtained results indicate significant power quality improvements in voltage imbalances and conversion losses thanks to the proposed backbone. Moreover, the study concludes with a discussion of the impact level of EVs and... [more]

This paper explores ways to save energy in households with energy prosumers who generate energy using photovoltaic panels and heat pumps. On the basis of a literature analysis, we formulated a research gap in the case of the energy behaviors of prosumers. This research is important due to the growing demand for energy and the transitions of countries toward renewable energy sources. The role of prosumers in the economy is growing as they ensure energy independence and cost savings. The main purpose of this research is to understand the energy behaviors of prosumers and to examine the differences in energy behaviors between users of photovoltaic systems and heat pumps. A sample of 326 Polish prosumer households was selected using the CAWI method in order to obtain empirical data. The results suggest that prosumers show advanced ecological behaviors, and more than half of the respondents implement pro-ecological behaviors in their homes. Being a prosumer is associated with energy indepen... [more]

The energy price influences the interest in investment, which leads to economic development. An estimate of the future energy price can support the planning of industrial expansions and provide information to avoid times of recession. This paper evaluates adaptive boosting (AdaBoost), bootstrap aggregation (bagging), gradient boosting, histogram-based gradient boosting, and random forest ensemble learning models for forecasting energy prices in Latin America, especially in a case study about Mexico. Seasonal decomposition of the time series is used to reduce unrepresentative variations. The Optuna using tree-structured Parzen estimator, optimizes the structure of the ensembles through a voter by combining several ensemble frameworks; thus an optimized hybrid ensemble learning method is proposed. The results show that the proposed method has a higher performance than the state-of-the-art ensemble learning methods, with a mean squared error of 3.37 × 10−9 in the testing phase.