Short-term Photovoltaic (PV) Power Forecasting (STPF) is considered a topic of utmost importance in smart grids. The deployment of STPF techniques provides fast dispatching in the case of sudden variations due to stochastic weather conditions. This paper presents an efficient data-driven method based on enhanced Random Forest (RF) model. The proposed method employs an ensemble of attribute selection techniques to manage bias/variance optimization for STPF application and enhance the forecasting quality results. The overall architecture strategy gathers the relevant information to constitute a voted feature-weighting vector of weather inputs. The main emphasis in this paper is laid on the knowledge expertise obtained from weather measurements. The feature selection techniques are based on local Interpretable Model-Agnostic Explanations, Extreme Boosting Model, and Elastic Net. A comparative performance investigation using an actual database, collected from the weather sensors, demonstra... [more]

The high penetration of photovoltaic (PV) systems and fast communications networks increase the potential for PV inverters to support the stability and performance of microgrids. PV inverters in the distribution network can work cooperatively and follow centralized and decentralized control commands to optimize energy production while meeting grid code requirements. However, there are older autonomous inverters that have already been installed and will operate in the same network as smart controllable ones. This paper proposes a decentralized optimal control (DOC) that performs multi-objective optimization for a group of PV inverters in a network of existing residential loads and autonomous inverters. The interaction of independent DOC groups in the same network is considered. The limit of PV inverter power factor is included in the control. The DOC is done by the power flow calculation and an autoregression prediction model for estimating maximum power point and loads. Overvoltage cau... [more]

This article’s aim is to explain the impact of technology entrepreneurship phenomenon on waste management enterprise performance in the conditions of COVID-19 pandemic. The concept of technology entrepreneurship according to the configuration approach and the category of high-performance organization are the theoretical bases of empirical investigation. For the implementation of empirical research, Fuzzy set Qualitative Comparative Analysis (FsQCA) was adopted. The research sample included a group of producers of Refused Derived Fuel (RDF) as a central part of the waste to energy industry located in Poland. The research results showed that the waste to energy sector is highly immune to pandemic threats. While during COVID-19, the basic economic parameters (i.e., sales, profitability and employment) of the entire industry in Poland clearly decreased, the same parameters in the case of the waste to energy industry remained at the same level. The research results allow the formulation of... [more]

Power flow analysis is an inevitable methodology in the planning and operation of the power grid. It has been performed for the transmission system, however, along with the penetration of the distributed energy resources, the target has been expanded to the distribution system as well. However, it is not easy to apply the conventional method to the distribution system since the essential information for the power flow analysis, say the impedance and the topology, are not available for the distribution system. To this end, this paper proposes an alternative method based on practically available parameters at the terminal nodes without the precedent information. Since the available information is different between high-voltage and low-voltage systems, we develop two various machine learning schemes. Specifically, the high-voltage model incorporates the slack node voltage, which can be practically obtained at the substation, and yields a time-invariant model. On the other hand, the low vo... [more]

Solar energy production based on a photovoltaic system is closely related to solar irradiance. Therefore, the planning of production is based on the prediction of solar irradiance. The optimal use of different energy storage systems requires an accurate prediction of solar irradiation with at least an hourly time horizon. In this work, a solar irradiance prediction method is developed based on the prediction of solar shading by clouds. The method is based on determining the current cloud position and estimating the velocity from a sequence of multiple images taken with a 180-degree wide-angle camera with a resolution of 5 s. The cloud positions for the next hour interval are calculated from the estimated current cloud position and velocity. Based on the cloud position, the percentage of solar overshadowing by clouds is determined, i.e., the solar overshadowing curve for the next hour interval is calculated. The solar irradiance is determined by normalizing the percentage of the solar u... [more]

In this paper we analyzed the link between companies’ performance, in terms of cash and income, and the labor productivity or management rates, in case of the companies from the energy sector listed on the Bucharest Stock Exchange. We focused on the energy sector because of the impact that its expansion has on the evolution of economies around the world and because of its dynamics in the sense of gradually shifting to the use of energy from renewable sources. We have used panel regression models to analyze the operating cash flow and the profitability rates and the determination of a causal or dependency relationship with labor productivity or management rates. The results of this study show a significant negative correlation between operating cash flows and the average duration of stock rotation, and no correlation between productivity and the operating cash flow. Instead, the average duration of stock turnover does not at all influence the profitability rates, and productivity is alw... [more]

The paper presents a power-quality analysis in the utility low-voltage network focusing on harmonic currents’ pollution. Usually, to forecast the modern electrical and electronic devices’ contribution to increasing the current total harmonic distortion factor (THDI) and exceeding the regulation limit, analyses based on tests and models of individual devices are conducted. In this article, a composite approach was applied. The performance of harmonic currents produced by sets of devices commonly used in commercial and residential facilities’ nonlinear loads was investigated. The measurements were conducted with the class A PQ analyzer (FLUKE 435) and dedicated to the specialized PC software. The experimental tests show that the harmonic currents produced by multiple types of nonlinear loads tend to reduce the current total harmonic distortion factor (THDI). The changes of harmonic content caused by summation and/or cancellation effects in total current drawn from the grid by nonlinear l... [more]

With the development of smart power grids, electronic transformers have been widely used to monitor the online status of power grids. However, electronic transformers have the drawback of poor long-term stability, leading to a requirement for frequent measurement. Aiming to monitor the online status frequently and conveniently, we proposed an attention mechanism-optimized Seq2Seq network to predict the error state of transformers, which combines an attention mechanism, Seq2Seq network, and bidirectional long short-term memory networks to mine the sequential information from online monitoring data of electronic transformers. We implemented the proposed method on the monitoring data of electronic transformers in a certain electric field. Experiments showed that our proposed attention mechanism-optimized Seq2Seq network has high accuracy in the aspect of error prediction.

The stochastic behavior of PV together with high PV penetration have given rise to power quality concerns involving voltage dynamic issues such as undervoltage, overvoltage, sag and swell. To ensure the grid’s stability, various methods have been practiced such as a proper sizing of the grid lines and the installation of power quality compensation equipment. However, these measures often require high costs and high control complexity due to additional equipment being involved such as multiple transformers and inverters. Moreover, the current available reactive power compensation equipment has a lesser impact on distribution level networks. Therefore, this work proposes a hybrid control of grid-feeding mode and energy storage with Direct Current (DC) fault detection scheme utilizing fuzzy control to mitigate high PV penetration problems, PV intermittency and faults via active power compensation to maintain the system’s voltage within its nominal range. This hybrid control works on two m... [more]

Because of the high penetration of renewable energies and the installation of new control devices, modern distribution networks are faced with voltage regulation challenges. Recently, the rapid development of artificial intelligence technology has introduced new solutions for optimal control problems with high dimensions and dynamics. In this paper, a deep reinforcement learning method is proposed to solve the two-timescale optimal voltage control problem. All control variables are assigned to different agents, and discrete variables are solved by a deep Q network (DQN) agent while the continuous variables are solved by a deep deterministic policy gradient (DDPG) agent. All agents are trained simultaneously with specially designed reward aiming at minimizing long-term average voltage deviation. Case study is executed on a modified IEEE-123 bus system, and the results demonstrate that the proposed algorithm has similar or even better performance than the model-based optimal control sche... [more]

Integration of electric vehicles into electric power system brings both challenges and solutions in the operation of power grids. On the one hand, simultaneously charging a large number of electric vehicles causes branch congestion or large voltage drop. Operating the electric vehicles in the discharging mode, on the other hand, introduces the provision of several ancillary services like peak power shaving and spinning reserves. From the electric vehicles operation point of view, thus, the distribution system operators require a real-time monitoring infrastructure to capture the states of electric vehicle chargers and accordingly operate their grids in the safe mode with respect to the power quality standards (e.g., EN 50160). In this context, the real-time smart charging and storage platform of the EU Horizon 2020 “MEISTER” project, based on the information and communication technology, manages the availability of electric vehicles as a potential source of energy in the need of one or... [more]

The increasing penetration level of distributed energy resources (DERs) increases the risk of congestion in the distribution network. To mitigate this, the concept of the small-scale DER aggregator was introduced as a change from uncoordinated to coordinated DERs. However, without appropriate network use cost allocation, the unwanted DER curtailment will be enforced by the network operator. Therefore, this paper proposes a new approach for congestion management by allocating the different network usage costs depending on how much congestion is caused by the DERs in the distribution network. For this, a modified Kirschen’s tracing method is proposed and applied to the small-scale DER aggregator market. To verify the effectiveness of the proposed method, a simulation of the small-scale DER aggregator market in South Korea was performed under the IEEE 69-bus distribution network. The model was able to allocate the different network usage costs at different buses and, thus, encouraged the... [more]

A comprehensive assessment of smart grids is critical for their development. Existing scientific research testifies to the urgency and complexity of the problem of implementing smart grids effectively, both in terms of a single project performance and from the standpoint of creating a local, and later global, energy system. The multidimensionality of smart grids makes it challenging to assess the effectiveness of their implementation. Difficulties in evaluation arise because it is challenging to consider technical, technological, economic, and other relevant aspects of smart grids’ development within a single evaluation system. There are currently a significant number of smart grid assessment systems. However, it remains debatable how systematically and comprehensively they measure the efficiency of a smart grid. This, in turn, raises the question of whether there is a universal evaluation system that integrally considers all the crucial components of smart grids and is suitable for ev... [more]

Electricity Distributions Networks (DNs) are changing from a once passive to an active electric power system element. This change, driven by several European Commission Directives and Regulations in the energy sector prompts the proliferated integration of new network elements, which can actively participate in network operations if adequately utilized. This paper addresses the possibility of using these active DN elements for optimization of a time-discrete network operation in terms of minimization of power losses while ensuring other operational constraints (i.e., voltage profiles and line currents). The active elements considered within the proposed optimization procedure are distributed generation units, capable of reactive power provision; remotely controlled switches for changing the network configuration; and an on-load tap changer-equipped substation, supplying the network. The proposed procedure was tested on a model of an actual medium voltage DN. The results showed that sim... [more]

Energy load forecasting for optimization of chiller operation is a topic that has been receiving increasing attention in recent years. From an engineering perspective, the methodology for designing and deploying a forecasting system for chiller operation should take into account several issues regarding prediction horizon, available data, selection of variables, model selection and adaptation. In this paper these issues are parsed to develop a neural forecaster. The method combines previous ideas such as basis expansions and local models. In particular, hyper-gaussians are proposed to provide spatial support (in input space) to models that can use auto-regressive, exogenous and past errors as variables, constituting thus a particular case of NARMAX modelling. Tests using real data from different world locations are given showing the expected performance of the proposal with respect to the objectives and allowing a comparison with other approaches.

With the introduction of the renewable energy communities in the current electrical market environment, it becomes possible to aggregate small generation resources and users’ loads to exchange power within the aggregation and at the same time provide services to the electrical system. The renewable energy community of users equipped with nanogrid technology allows performing an adequate level of flexibility. It may be the solution to coordinate in the best possible way the energy resources in order to increase the community self-consumption and to provide ancillary services to the grid. In this paper, a model for the interaction between the Distribution System Operator (DSO)—Transmission System Operator (TSO) and the energy community based on nanogrids is proposed and an operational example is presented.

Since the outbreak of COVID-19 and the American decoupling policy, the global value chains (GVCs) have been switched to regional GVCs, and, in the worst case, are subject to a potential alteration of reversing the GVCs, ultimately entailing a severe impact on international trade and the global energy market. This paper applies a quantitative approach using a computational general equilibrium (CGE) model to estimate the effects of the reverse GVC factors on the global economy, trade, and energy market. These reverse GVC factors will decrease the global GDP, and such effect will bring a greater influence on both China as well as the United States, which is pursuing decoupling. The increased trade costs due to these factors will reduce the GVC indices, mostly in ASEAN by 0.2~1.15%, followed by Korea, Japan and China. Surprisingly, the GVC index in the United States is expected to be strengthened due to the enhanced GVC with its allies such as Canada and Mexico. In China, the use of oil, g... [more]

This paper analyzes the energy reform that has taken place in Mexico since 2013, driven by steady growth in energy demand and insufficient economic resources. The relevant points in the restructuring process are discussed, shedding light on the impact of recent governmental actions not aligned with the original spirit of the law. This research uses a framework and fundamentals of a well-organized structural process called the textbook model, making a comparative analysis of Mexican reform. It proceeds by presenting the Mexican Electrical System in numbers and how it is affected by the present government’s restructuring process providing positive and negative impacts of several implementations. The main objectives of restructuring were carried out to attract private investment and increase the reliability and efficiency of the system. During the first four years, the reform has attracted investment, in diminishing form in generation but not in transmission and distribution. Therefore, t... [more]

Smart grid endpoints need to use two environments within a processing system (PS), one with a Linux-type operating system (OS) using the Arm Cortex-A53 cores for management tasks, and the other with a standalone execution or a real-time OS using the Arm Cortex-R5 cores. The Xen hypervisor and the OpenAMP framework allow this, but they may introduce a delay in the system, and some messages in the smart grid need a latency lower than 3 ms. In this paper, the Linux thread latencies are characterized by the Cyclictest tool. It is shown that when Xen hypervisor is used, this scenario is not suitable for the smart grid as it does not meet the 3 ms timing constraint. Then, standalone execution as the real-time part is evaluated, measuring the delay to handle an interrupt created in programmable logic (PL). The standalone application was run in A53 and R5 cores, with Xen hypervisor and OpenAMP framework. These scenarios all met the 3 ms constraint. The main contribution of the present work is... [more]

The planning and realization of wind energy projects requires an as accurate and precise wind resource estimation as possible. Standard procedures combine shorter on-site measurements with the application of numerical models. The uncertainties of the numerical data generated from these models are, particularly in complex onshore terrain, not just rather high but typically not well quantified. In this article we propose a methodology for using a single scanning Doppler wind lidar device to calibrate the output data of a numerical flow model and with this not just quantify but potentially also reduce the uncertainties of the final wind resource estimate. The scanning lidar is configured to perform Plan Position Indicator (PPI) scans and the numerical flow data are projected onto this geometry. Deviations of the derived from the recorded line-of-sight wind speeds are used to identify deficiencies of the model and as starting point for an improvement and tuning. The developed methodology i... [more]

The aim of this article was to identify the role of good mutual relationships with offerors for final purchasers, as well as define the meaning of the perception of offerors in the scope of listening to purchasers’ opinions and profiting from purchasers’ readiness to cooperate for the specificities of the prosumeric activity. A deep analysis of the world literature was used to prepare the theoretical part of this paper. The results of this analysis confirm the existing cognitive gap and research gap regarding mentioned aspects, including energy market. Empirical studies were conducted to reduce identified gaps. The survey method was used to collect primary data. The collected data were subjected to quantitative analysis, during which statistical analysis methods and tests were applied (Pearson chi-square independence test, V-Cramer factor analysis, Kruskal−Wallis test (KW), and exploratory factor analysis). The results of the statistical analysis and testing allowed the three research... [more]

Electrical power systems represent a fundamental part of society, and their efficient operations are of vital importance for social and economic development. Power systems have been designed to withstand interruptions under already provided safety and quality principles; however, there are some extreme and not so frequent events that could represent inconveniences for the correct operation of the entire system. For this reason, in recent years the term resilience, which serves to describe the capacity of a system to recover from an unwanted event, has been analyzed on planning, operation and remedial actions. This work is focused on the implementation of a topological reconfiguration tool, which is oriented to change the structure of primary feeders based on changing the status of switchgears. Once the distribution network has been reconfigured, an algorithm of protection coordination is executed based on communication peer-to-peer between Matlab and PowerFactory, which develops an ada... [more]

Recent studies suggest that decomposing a series of electricity spot prices into a trend-seasonal and a stochastic component, modeling them independently, and then combining their forecasts can yield more accurate predictions than an approach in which the same parsimonious regression or neural network-based model is calibrated to the prices themselves. Here, we show that significant accuracy gains can also be achieved in the case of parameter-rich models estimated via the least absolute shrinkage and selection operator (LASSO). Moreover, we provide insights as to the order of applying seasonal decomposition and variance stabilizing transformations before model calibration, and propose two well-performing forecast averaging schemes that are based on different approaches for modeling the long-term seasonal component.

From the perspective of stable operation of the power transmission system, the transmission system operators (TSO) needs to procure reserve adjustment power at the stage of the previous day based on solar power forecast information from global horizontal irradiance (GHI). Because the reserve adjustment power is determined based on information on major outliers in past forecasts, reducing the maximum forecast error in addition to improving the average forecast accuracy is extremely important from the perspective of grid operation. In the past, researchers have proposed various methods combining the numerical weather prediction (NWP) and machine learning techniques for the one day-ahead solar power forecasting, but the accuracy of NWP has been a bottleneck issue. In recent years, the development of the ensemble prediction system (EPS) forecasts based on probabilistic approaches has been promoted to improve the accuracy of NWP, and in Japan, EPS forecasts in the mesoscale domain, called m... [more]

Distribution networks transmit electrical energy from an upstream network to customers. Undesirable circumstances such as faults in the distribution networks can cause hazardous conditions, equipment failure, and power outages. Therefore, to avoid financial loss, to maintain customer satisfaction, and network reliability, it is vital to restore the network as fast as possible. In this paper, a new fault location (FL) algorithm that uses the recorded data of smart meters (SMs) and smart feeder meters (SFMs) to locate the actual point of fault, is introduced. The method does not require high-resolution measurements, which is among the main advantages of the method. An impedance-based technique is utilized to detect all possible FL candidates in the distribution network. After the fault occurrence, the protection relay sends a signal to all SFMs, to collect the recorded active power of all connected lines after the fault. The higher value of active power represents the real faulty section... [more]