Electricity

The battery-based multilevel inverter has grown in popularity due to its ability to boost a system’s safety while increasing the effective battery life. Nevertheless, the system’s high degree of freedom, induced by a large number of switches, provides difficulties. In the past, central computation systems that needed extensive communication between the master and the slave module on each cell were presented as a solution for running such a system. However, because of the enormous number of slaves, the bus system created a bottleneck during operation. As an alternative to conventional multilevel inverter systems, which rely on a master–slave architecture for communication, decentralized controllers represent a feasible solution for communication capacity constraints. These controllers operate autonomously, depending on local measurements and decision-making. With this approach, it is possible to reduce the load on the bus system by approximately 90 percent and to enable a balanced state of charge throughout the system with an absolute maximum standard deviation of $1.1\times {10}^{-5}$. This strategy results in a more reliable and versatile multilevel inverter system, while the load on the bus system is reduced and more precise switching instructions are enabled. Full article

Recently, the danger of a long blackout is discussed in Europe. Blackouts can be caused by failures in the energy distribution, errors in large power plants or even cyber-attacks. This can lead to a chain reaction and a disintegration of the mains. Longer blackouts have an extreme impact on the economy as a whole and on local households. Therefore, a small local grid at home which can supply the most important loads over some time has garnered increasing interest. With a small direct current (DC) grid, critical loads such as for deep freezers and refrigerators can be supplied, and some LED lights can be used in the evening or at night. Solar generators (panels) can be used to charge energy storage devices, e.g., batteries. A DC grid can not only be used in the case of an emergency, but can also be used to reduce energy consumption out of the public mains and reduce energy bills. The architecture of the household emergency DC grid is discussed; suggestions for batteries are given; two simple chargers, based on DC-DC-converters like the Buck (step-down) and on the Boost (step-up) converters, are shown; dimensioning suggestions are given; and simple, robust controllers, a P-controller with disturbance feedforward and a hysteresis controller, are treated and tested via simulations. The goal of the paper is to show a simple autonomous home energy system without an external fieldbus, LAN or internet connection with special focus on simple charger topologies. Full article

We have developed a conventional benchmark model for the prediction of two days of electricity consumption for industrial and institutional customers of an electricity provider. This task of predicting 96 values of 15 min of electricity consumption per day in one shot is successfully dealt with by a dynamic regression model that uses the Seasonal and Trend decomposition method (STL) for the estimation of the trend and the seasonal components based on (approximately) three years of real data. With the help of suitable R packages, our concept can also be applied to comparable problems in electricity consumption prediction. Full article

This paper investigates the potential of bidirectional charging using modular multilevel inverter-based reconfigurable battery systems via grid-parallel control. The system offers several advantages such as modularity, scalability, and fault-tolerance over conventional battery electric vehicle systems. It is designed for seamless integration with the grid, allowing bidirectional power flow and efficient energy storage. Within this study, the battery system is first simulated in Matlab/Simulink and later implemented into a hardware setup. Eventually, the simulation results and the measurements have been compared and evaluated. Thereby, startup sequences and constant current scenarios were investigated. It has been shown that the system is fully capable to charge and discharge the batteries in the grid-parallel connection, thus enabling bidirectional charging with close to full drive system power. The current total harmonic distortion complies with grid regulations and can potentially improve the grid quality. The proposed system offers significant potential for grid-integrated energy storage systems, addressing the challenges associated with renewable energy integration, grid stability, and energy management. In comparison to other publications on this topic, the proposed approach does not need additional dedicated power electronic hardware and has more degrees of freedom for current control. Full article

Wide-Area Power System Stabilizers (WAPSSs) are damping controllers used in power systems that employ data from Phasor Measurement Units (PMUs). WAPSSs are capable of providing high damping rates for the low-frequency oscillation modes, especially the inter-area modes. Oscillation modes can destabilize power systems if they are not correctly identified and adequately damped. However, WAPSS communication channels may be subject to failures or cyber-attacks that affect their proper operation and may even cause system instability. This research proposes a method based on an optimization model for the design of a WAPSS robust to multiple permanent communication failures. The results of applications of the proposed method in the IEEE 68-bus system show the ability of the WAPSS design to be robust to a possible number of permanent communication failures. Above this value, the combinations of failures and processing time are high and they make it difficult to obtain high damping rates for the closed-loop control system. The application and comparison of different optimization techniques are valid and showed a superior performance of the Grey Wolf Optimizer in solving the optimization problem. Full article

Enhancing electric vehicle infrastructure by forecasting the availability of charging stations can boost the attractiveness of electric vehicles. The transportation sector plays a crucial role in battling climate change. The majority of available prediction algorithms either achieve poor accuracy or predict the availability at certain points in time in the future. Both of these situations are not ideal and may potentially hinder the model’s applicability to real-world situations. This paper provides a new model for estimating the charging duration of charging events in real time, which may be used to estimate the waiting time of users at fully occupied charging stations. First, the prediction is made using the random forest regressor (RF), and then the prediction is enhanced utilizing the findings of the RF model and real-time information of the currently occurring charging events. We compare the proposed method with the RF model, which is the approach’s foundational model, and the best-performing prediction model of the light gradient boosting machine (LightGBM). Here, we make use of historical information of charging events gathered from 2079 charging stations across Germany’s 4602 fast-charging connectors. To reduce data bias, we specifically simulate prediction requests for 30% of the charging events with various characteristics that were not trained with the model. Overall, the suggested method performs better than both the RF and the LightGBM. In addition, the model’s structure is adaptable and can incorporate real-time information on charging events. Full article

In isolated power systems with very high instantaneous shares of renewables, additional inertia should be used as a complementary resource to battery energy storage systems (BESSs) for improving frequency stability, which can be provided by synchronous condensers (SCs) integrated into the system. Therefore, this paper presents a methodology to infer the system dynamic security, with respect to key frequency indicators, following critical disturbances. Of particular interest is the evidence that multiple short-circuit locations should be considered as reference disturbances regarding the frequency stability in isolated power grids with high shares of renewables. Thus, an artificial neural network (ANN) structure was developed, aiming to predict the network frequency nadir and Rate of Change of Frequency (RoCoF), considering a certain operating scenario and disturbances. For the operating conditions where the system frequency indicators are violated, a methodology is proposed based on a gradient descent technique, which quantifies the minimum amount of additional synchronous inertia (SCs which need to be dispatch) that moves the system towards its dynamic security region, exploiting the trained ANN, and computing the sensitivity of its outputs with respect to the input defining the SC inertia. Full article

We propose a method for the fault passage indication of earth faults in resonant-earthed networks, based on phase current measurements alone. This is particularly relevant for electricity distribution systems at medium-voltage levels. The method is based on the relative magnitudes of the phasor changes in the phase currents due to the fault. It is tested for various network types and operation configurations by simulating the network in pscad and using the simulated currents as the input for an implementation of the method in matlab. In over-compensated networks, the method shows reliable detection of the fault passage, with good selectivity and sensitivity for both homogeneous and mixed (cable and overhead line) feeders. However, for the less common under-compensated systems, it has limitations that are described further in this study. The method has good potential for being cost effective since it requires only current measurements, from a single location, at a moderate sampling rate. Full article

This paper presents a method for integrating industrial consumers owning compensation systems as alternative reactive power sources into grid operating processes. In remuneration, they receive a market-based provision of reactive power. The aim is to analyze the potential of reactive power compensation systems of industrial companies connected to medium-voltage (10 kV–30 kV) AC grids in order to increase the reactive power ability of distribution grids. Measurement methods and reactive power potential results of six industrial companies are presented to characterize the amount and temporal availability of their reactive power potential. The presented approach for using the decentralized reactive power potential is a centralized reactive power control method and is based on optimal power flow (OPF) calculations. An optimization algorithm based on linear programming is used to coordinate a reactive power retrieval tuned to the actual demand. The influencing quantities are the current grid status (voltage and load flow capacity reserves at grid nodes and power lines) and the current reactive power potential of the reactive power sources. The compensation impact of six measured industrial companies on an exemplary medium-voltage grid is shown by an application example. Full article

The increase in the average global temperature is a consequence of high greenhouse gas emissions. Therefore, using alternative energy carriers that can replace fossil fuels, especially for automotive applications, is of high importance. Introducing more electronics into an automotive battery pack provides more precise control and increases the available energy from the pack. Battery-integrated modular multilevel converters (BI-MMCs) have high efficiency, improved controllability, and better fault isolation capability. However, integrating the battery and inverter influences the maximum DC charging power. Therefore, the DC charging capabilities of 5 3-phase BI-MMCs for a 40-ton commercial vehicle designed for a maximum tractive power of 400 kW was investigated. Two continuous DC charging scenarios are considered for two cases: the first considers the total number of submodules during traction, and the second increases the total number of submodules to ensure a maximum DC charging voltage of 1250 V. The investigation shows that both DC charging scenarios have similar maximum power between 1 and 3 MW. Altering the number of submodules increases the maximum DC charging power at the cost of increased losses. Full article

Transportation electrification can significantly reduce carbon footprint and accelerate the modernization of aging electric infrastructure. In the U.S., the growing adoption of electric vehicles (EVs) will significantly impact the electrical grid and associated greenhouse gas emissions, but with significant differences between the balancing regions due to the diverse characteristics of their electrical grids. This work assesses the impacts associated with the increasing penetration of EVs in the U.S., considering the characteristics of the grid in the different regions, in order to discuss the needed strategies to maximize the future decarbonization benefits. The assessment considers the variation in generation mix profiles during the day in each region, as well as different charging profiles associated with home, work, and public charging. The results show that more ambitious policies for the increasing share of carbon-free generation in the regions with the highest emissions are needed, emphasizing incentives for the use of work and public charging, and ensuring effective management of the charging flexibility. Full article

This paper presents a live field experience of creating an isolated microgrid for the Expoelectric fair during 2018 and 2019. The islanded microgrid comprises a Master Inverter with grid-forming capabilities and fault management. The Master Inverter and stationary batteries, and EVs with V2G capabilities provide storage. A PV generation system supplies the microgrid. The loads are the fair booths, mainly lighting and chargers for personal mobility vehicles. All the equipment used in the experimental microgrid is from different manufacturers. The operation and control of the islanded microgrid are based on the VDE-AR-N-4105 standard. The paper also presents the operation of the Master Inverter during faults. The live field experience shows that the proposed operation method is valid for operating different converters from different manufacturers without needing any communication layer between them. The experimental results also show that faults can be handled correctly by the Master Inverter to operate the entire microgrid safely. In conclusion, islanded microgrids based on power electronics are feasible to replace diesel generators in faires, conventions or temporary events. Full article

This paper presents a novel tool for electricity planning, based on an improvement of MuSIASEM (Multiscale Integrated Analysis of the Societal and Ecological Metabolism) by incorporating a new regional analysis of the electricity metabolism across levels. An analysis of Mexico illustrates this toolkit and shows that the industry sector has economic energy intensity (EEI) with 40.3 MWh/MMXN reaching a higher value than the commerce and services sector with 0.84 MWh/MMXN. Regarding the economic labor productivity (ELP) indicator (AV/h), the industrial sector with 208.5 TMXN/Kh reached a higher value than the commercial and services sector with 114.3 TMXN/Kh. Regarding the exosomatic metabolic rate (EMR), the household sector obtained 59.3 KWh/Kh, whereas the economic sector reached 2486.4 KWh/Kh. Disaggregation of the EMR indicator into economic sectors shows that the industrial sector reached 8.4 KWh/Kh and the commercial and services sector reached 0.10 KWh/Kh. The lack of complete data for the agricultural sector does not allow us to calculate EEI, ELP, and EMR indicators accurately. This innovative approach is useful for governance because it helps us to understand and reduce asymmetries across regions in terms of electricity consumption, resulting in more social equality and a better economic equilibrium across sectors and regions. Full article

This paper presents a stochastic approach to assessing the hosting capacity for solar PV. The method is part of the optimal techniques for the integration of renewables. There are two types of uncertainties, namely aleatory and epistemic uncertainties. The epistemic and aleatory uncertainties influence distribution networks’ hosting capacity differently. The combination of the two uncertainties influences the planning of distribution networks. The study introduces and considers the epistemic probability distribution function (PDF). DSO does take levels of risk for a parameter violation when planning. Epistemic PDF is a range of values of the planning risk margin for quantifying the hosting capacity. The planning risk acknowledges that overvoltages may occur at weaker conceivable locations in a distribution network. In the paper, it has been shown that the number of customers who will be able to connect solar PV in future is influenced by the DSO’s planning risk margin. The DSO can be stricter or less strict in planning risk margin. It has been concluded that fewer customers can connect solar PV to a distribution network when a DSO takes a stricter planning risk. Alternatively, more customers can connect solar PV units for a less strict planning risk. How stricter or less strict the DSO is with the planning risk margin determines the investment needed for mitigation measures. The mitigation measures in the future will lead to not exceeding the overvoltage limit when solar PV is connected to the weaker conceivable points of the distribution network. Full article

The actual research in terms of energy focuses drastically on the use of green energy resources. Hydropower systems have been the most known green sources for years. However, the hydropower systems, which are seasonal and most exploited, do not cover the speed of increasing daily demand. The injection of solar power could be a supporting alternative, but it is only in daylight, weather dependent and intermittent. Therefore, a storage system is required. The batteries are the quick recourse. Not only the energy sector, but also the transport systems are not left behind; they are striving to turn green. Therefore, they are turning to electric vehicles (EVs) and electric moto-bicycles (EMBs). On the other hand, this option tends to be a sharply increasing demand that can be a burden to the grid, i.e., the increase in the EVs and EMBs implies increases in power demand, grid components and pressure on the grid. Fortunately, the EVs use batteries to store energy for their use. Therefore, the EVs are the power storage system, they become part of the power management system and they can save the power surplus. With the injection of PV solar power, there is no need for an extra storage system, as the EVs are charged from the grid and store the solar energy that can be used later after sunset. The bi-directional off-board charger is a solution as it allows the grid to charge the vehicle (G2V) and the vehicle to send power back to grid (V2G). The inclusion of EVs in power management introduces the concept of vehicle-to-vehicle (V2V) when one EV can charge another, and the vehicle-to-load (V2X) where the EV can supply power to EMBs or any load. The V2G, G2V, V2X, the inclusion on solar energy to the grid and the behavior of the grid in that scenario will be illustrated in this paper. Full article

The operation of electrical networks, microgrids, or heterogeneous battery systems, especially the dispatch of single units within the system, requires sophisticated power flow control strategies. If objectives such as efficiency are demanded for the operation of the energy system, typical control strategies lack the ability to verify the optimality of the operation. Dynamic programming is a widely used method for determining the global optima of trajectory problems. In the context of energy systems and power flow optimization, it is restricted to applications with a low number of states and decisions. The reason for this is the rapid growth of computational effort with increasing dimensionality of the state and decision space. The approach of iterative dynamic programming (iDP) makes dynamic programming applicable to the planning and benchmarking of complex power flow optimization problems. To illustrate this, a heterogeneous battery energy storage system is introduced for which the iDP optimizes the power split at the point of common coupling to minimize the total cumulative loss of energy. The method can be adopted for a broad range of energy systems such as microgrids, utility grids, or electric vehicles. The applicability is limited only by the computation time, which depends on the model complexity and the length of the time series. To verify the functionality of the iterative dynamic programming, its results are directly compared to those of the standard dynamic programming. The total computation time can be reduced by 98% in the tested scenario. As relevant use cases, static and dynamic methods of power sharing are validated and benchmarked. The iDP offers a novel and computationally efficient method for the design and validation of power flow control strategies. Full article

With the evolution of electromobility and heat pumps in urban areas, distribution system operators find themselves facing new challenges in reinforcing their grids. With this evolution, the power demand is developing rapidly and grid reinforcement is urgently needed. The electromobility and heat pump loads are introduced by giving the assumed development scenarios in Germany and their corresponding nominal power assumptions. Furthermore, a method for load modeling in grid planning is explained. Subsequently, several grid planning approaches are presented while dividing them into conventional and innovative planning strategies. Among the investigated innovative planning strategies are three variants of load management that regulate different load types. By analyzing several urban medium voltage grids, this contribution deduces a solid basis for distribution system operators in the form of planning guidelines. The implemented grid planning method leading to the planning guidelines is presented in detail along the contribution. Full article

Accurate electricity demand forecasting is vital to the development and evolution of smart grids as well as the reinforcement of demand side management strategies in the energy sector. Since this forecasting task requires the efficient processing of load profiles extracted from smart meters for large sets of clients, the challenges of high dimensionality often lead to the adoption of cluster-based aggregation strategies, resulting in scalable estimation models that operate on aggregate times series formed by client groups that share similar load characteristics. However, it is evident that the clustered time series exhibit different patterns that may not be processed efficiently by a single estimator or a fixed hybrid structure. Therefore, ensemble learning methods could provide an additional layer of model fusion, enabling the resulting estimator to adapt to the input series and yield better performance. In this work, we propose an adaptive ensemble member selection approach for stacking and voting regressors in the cluster-based aggregate forecasting framework that focuses on the examination of forecasting performance on peak and non-peak observations for the development of structurally flexible estimators for each cluster. The resulting ensemble models yield better overall performance when compared to the standalone estimators and our experiments indicate that member selection strategies focusing on the influence of non-peak performance lead to more performant ensemble models in this framework. Full article