István Vokony

Large multinational energy companies face a strategic trilemma in balancing growth, strategy, and sustainability, which are often difficult to integrate and insufficiently supported by measurable performance indicators. Despite the extensive literature on Sustainability Balanced Scorecard (SBSC) models, prior frameworks remain limited in their ability to differentiate human-related performance drivers, to embed risk management logic structurally, and to reflect sector-specific characteristics of the energy industry. This study aims to develop a structured, metrics-based framework that strengthens sustainability-oriented strategic planning and decision-making. Adopting a design science research perspective, the study proposes an enhanced Balanced Scorecard model, the Sustainable Six-Perspective Balanced Scorecard (HEX-BSC). Building on the Sustainability Balanced Scorecard, the framework expands the traditional four perspectives to six: financial, external customer, internal customer (employee), corporate culture, internal processes, and sustainability. A human-centred structure separates employee satisfaction from organizational culture to improve diagnostic precision. A 4T risk-management module—Transfer, Terminate, Tolerate, Treat—is embedded within each perspective to support dynamic and responsive performance management. Qualitative feedback from senior leaders in the Hungarian energy sector indicates that the HEX-BSC is both conceptually robust and practically interpretable. By explicitly linking outcomes across perspectives–such as assessing how product launches affect sales, customer experience, employee workload, processes, and sustainability—the model enhances strategic alignment, transparency, and organizational learning. The novelty of the HEX-BSC lies in its explicit separation of employee and corporate culture perspectives, the integration of a 4T risk-management logic across all dimensions, and its energy-sector-specific design.

Renewable energy generation is spreading apace worldwide, bringing integration challenges for distribution system operators. The focus of this study is to investigate the possible solutions to increase the hosting capacity of an actual low voltage (LV) feeder in Hungary. This pilot site already has significant photovoltaic (PV) generation, but more installations are expected in the near future. Simulations were carried out to compare the available technical options increasing the hosting capacity. Detailed on-site measurements were taken, which included the neutral line, to have a better understanding of the basic situation. It showed that the voltage is close to the limits with significant unbalance. Therefore, an unbalanced model was adopted with a new calculation method in order to incorporate the neutral line and the groundings. The simulations compared local PV inverter control, inline voltage regulator, tap-changing transformer and phase balancer and found the inline voltage regulator as the most effective and versatile solution for this case. A new device was developed and installed that implements an inline voltage regulator, optimized for local circumstances. Then, a longer series of measurements were taken which proved the effectiveness of the unit, showing that it can be a cost-effective grid development alternative to grid reinforcement.

Megatrends, such as the proliferation of distributed generation, electrification, and the appearance of aggregator companies, put the low voltage power grids under intense pressure. Since the network infrastructure developments cannot keep up with the trends, distribution system operators turned to alternative solutions. Smart grid assets, such as on-load tap-changing distribution transformers or serial low voltage regulators, are promising solutions. However, the energy transition cannot be handled with the network expansive on the distribution level. Control centers are predicted to expand to this voltage level in the near future, and distribution system state estimation could be an enabler of all functionalities. On the low voltage level, data scarcity is a great challenge in observability; therefore, research must focus on the creation of pseudomeasurements and integration of available data sources. This paper examines the inclusion of smart assets from the conceptual point to the application on two sites, based on data from operational environments, both with a pseudomeasurement and an integrated metering point approach. The results showed that integrating smart assets could considerably mitigate voltage fluctuations, and reduce estimation errors by two magnitudes on the low voltage network.

The unprecedented transformation of contemporary power systems, mainly evidenced by the high penetration of renewable energy generation and the shift from passive to active, bi-directional smart grids, has put an extraordinary burden on power system operation and control. The uncertainties created by the two aforementioned factors greatly propel the necessity of more accurate and robust system monitoring. Concurrently, an ever-increasing amount of electronics-based power generation makes frequency stability a major challenge to power system operation and control due to the vastly diminishing system inertia. As a crucial part of system control, frequency monitoring is utilized to identify potential stability issues and eventually prevent cascading power outages and blackouts. This paper proposes a dynamic state estimation (DSE) methodology that employs deep learning (DL) techniques based on feed-forward artificial neural networks (ANN) to accurately predict power system disturbances in terms of frequency disturbance events (FDE) caused by line outages, load/generation tripping events, and various types of faults. The proposed methodology was tested on a test grid model with high penetration of renewable generation, using an open-source data generator. It was concluded that DL-based DSE can be successfully utilized in the FDE detection process to improve frequency monitoring and control, and to maintain optimal performance, stability, and security of power systems.

Distribution system state estimation is becoming an increasingly important feature in the modern power grid, but the lack of real measurements makes its implementation particularly difficult. A solution for this problem could be the implementation of Artificial Neural Networks for direct distribution system state estimation. In our work, we created an ANN-based distribution system state estimation which can function based on sparse data, the accuracy of the ANN-based state estimation can be further improved utilizing a separated distribution system state estimation (S-ANN), which estimates the state of the network separately according to measurement type based on the same input data. With this method, the root mean square error and standard deviation of the state estimation can be significantly decreased.

Solar energy is expected to have a key role in the transition to a cleaner and environment-friendlier energy system, but given its highly weather dependent and intermittent nature, it creates operational challenges for the electric power grid because of the uncertainty involved in the output. Thus renewable energy generation forecasting becomes an increasingly important task in regard to not just the reliable operation of the energy system, but also from a financial standpoint. Here it is analyzed how the information carried by the timestamp can be utilized in order to improve solar generation forecasting accuracy. We devised and implemented our own time feature input, which we tested on several techniques based on machine learning for solar energy generation forecasting. In this paper, we demonstrate that, though often overlooked, the information carried by the timestamp is significant and can greatly improve the forecasting accuracy.

The superior access to renewable sources in modern power systems increases the fluctuations in system voltage and power. Additionally, the central dilemmas in using renewable energy sources (RESs) are the intermittent nature of and dependence on wind speed and solar irradiance for wind and photovoltaic (PV) systems, respectively. Therefore, utilizing a vigorous and effective energy storage system (ESS) with RESs is crucial to overcoming such challenges and dilemmas. This paper describes the impacts of using a battery storage system (BSS) and superconducting magnetic energy storage (SMES) system on a DC bus microgrid-integrated hybrid solar–wind system. The proposed method employs a combination of BSS and SMES to improve the microgrid stability during different events, such as wind variation, shadow, wind turbine (WT) connection, and sudden PV outage events. Distinct control approaches are proposed to control the system’s different components in order to increase overall system stability and power exchange. Both the PV and wind systems are further equipped with unique maximum power point tracking (MPPT) controllers. Additionally, each of the ESSs is controlled using a proposed control method to supervise the interchange of the active power within the system and to keep the DC bus voltage constant during the different examined instabilities. Furthermore, to maintain the load voltage /frequency constant, the prime inverter is controlled using the proposed inverter control unit. The simulation results performed with Matlab/Simulink show that the hybrid BSS + SMES system successfully achieves the main targets, i.e., DC voltage, interchange power, and load voltage/frequency are improved and smoothed out. Moreover, a comparison among three case studies is presented, namely without using ESSs, using the BSS only, and once more using both BSS and SMES systems. The findings prove the efficacy of the proposed control method based on the hybrid BSS + SMES approach over BSS only in preserving the modern power system’s stability and reliability during the variable events.

A kutatásban új energiatermelő eszközökre alapozva a hő- és villamos energia biztosítása mellett helyben a szünetmentes ellátást szavatoló – telephelyen kívülre rendszerszintű szolgáltatás értékesítésére építő – üzemeltetési koncepciót alakítottunk ki. A szektorra jellemző nemzetközi tapasztalatok alapján teljes üzleti koncepciót szükséges kialakítani a sikeres pilotprojektekhez. A megvizsgált műszaki-gazdasági és regulációs környezet tendenciái alapján látható, hogy a ma még bizonytalan kimenetelű változások iránya egyöntetű: mind támogatja a szekunder szabályozásra alkalmas kiserőművi eszközök piaci térnyerését és profitabilitásának fennmaradását. Fontos feladat az unikális elemek azonosítása az értékajánlatokban. A kutatás célja annak feltárása, hogy a realizált árkülönbség, a hőoldali bevételek, a termelési kihasználtság, az optimalizált üzemviteli működtetés, a méretezés stb. közül melyik és milyen mértékben van hatással a beruházásra, az üzemeltetésre és összességében az üzleti modellre, annak eredményességére.

As renewable energy sources are spreading, the problems of energy usage, transport and storage arise more frequently. In order that the performance of energy producing units from renewable sources, which have a relatively low efficiency, should not be decreased further, and to promote sustainable energy consumption solutions, a living lab conception was elaborated in this project. At the pilot site, the produced energy (by PV panels, gas turbines/engines) is stored in numerous ways, including hydrogen production. The following uses of hydrogen are explored: (i) feeding it into the national natural gas network; (ii) selling it at a H-CNG (compressed natural gas) filling station; (iii) using it in fuel cells to produce electricity. This article introduces the overall implementation plan, which can serve as a model for the hybrid energy communities to be established in the future.