Inverters play an essential and indispensable role in energy conversion within modern electrical power systems. Conventional two-level inverters (2LIs) have been widely adopted across industrial applications due to their simple structure and mature control strategies. However, 2LIs exhibit several limitations when interfacing with utility-scale grid systems, including higher harmonic distortion, increased switching stress, and reduced efficiency in high-voltage and high-power operations. Multilevel Inverters (MLIs) have emerged as a transformative solution, enabling high-voltage and high-power applications with improved efficiency and significantly lower harmonic distortion compared to traditional two-level configurations. This review investigates the broad range of applications of 2LIs, along with their operational constraints, and provides a comprehensive overview of major MLI topologies, including neutral-point-clamped (NPC), flying-capacitor (FC), cascaded H-bridge (CHB), hybrid structures, transformer-based converters, and matrix converters. Key modulation and control techniques are also discussed, such as SPWM, SHE, SVPWM and random PWM. Furthermore, this paper highlights the practical deployment of MLIs in industrial motor drives, renewable energy integration, uninterruptible power supplies, and energy storage systems. Beyond technical aspects, global trends in inverter development are examined by comparing efficiencies, capabilities, and challenges among major manufacturers worldwide. Overall, MLIs are presented as scalable, efficient, and reliable converter solutions for the future of industrial and utility-scale power systems, offering valuable insight into current advancements and promising research directions.
The transformation of district heating systems in Germany is a central topic in energy and climate policy, given the 2045 climate neutrality targets. This study focuses on the main district heating system in the city of Göttingen in Germany, whose heat generation currently relies primarily on three Combined Heat and Power (CHP) plants subsidized under the German CHP Act, one wood chip boiler and two gas boilers. In 2025, the network’s heating demand amounted to 89 GWh, which was mainly covered by wood chips or biogas (41%) and natural gas (21%). This paper examines an option for increasing the share of renewable energy in the main district heating system of Göttingen. Therefore, this work analyses the integration of a power-to-heat system from a technical and economic perspective. The power-to-heat system consists of a Heat Pump (HP) that uses waste heat from a sewage treatment plant and is primarily powered by an off-grid wind turbine. The scenarios simulated vary in the temperature difference at the waste heat source and the part-load range of the HP. The optimum rated output of the HP is then determined based on the Levelized Cost of Heat (LCoH). The results indicate that the economic optimum is achieved with a HP of 2 MW capacity reaching a LCoH of 38.85 EUR/MWh. The integration of an on-site wind power plant ensures a self-sufficiency ratio above 0.55 in all investigated scenarios. Furthermore, the partial load range of the HP has a very positive effect on the LCoH. Overall, this analysis demonstrates a significant and economically viable decarbonization of the district heating system using local resources. Based on these results, it is recommended to investigate the potential benefits of integrating additional thermal or electrical storage systems. This could increase the share of renewable energies in the district heating system as well as the degree of self-sufficiency, and enable the provision of system services for the electricity market. Such measures could also contribute to a further reduction in the LCoH.
Limiting warming to the Paris temperature goals requires a rapid scale-up of low-carbon energy, yet recent experience suggests that deployment is increasingly shaped by delivery constraints rather than by technology cost trends alone. This review synthesizes peer-reviewed evidence on five constraints that repeatedly slow net-zero buildouts: lengthy approval and grid-connection processes; capital-intensive investment profiles that heighten sensitivity to the cost of capital and revenue risk; bottlenecks in critical minerals, processing, and manufacturing; social contestation and local governance that translate into siting exclusions, delays, and cancellations; and modeling traditions that can underrepresent these non-marginal frictions. Accordingly, we adopt an energy-services perspective to examine how demand-side strategies affect the scale and timing of the required buildout. We interpret degrowth as service-based sufficiency, rather than as a blanket reduction in welfare, and organize the evidence using the Avoid, Shift, Improve (ASI) sequence. Across sectors, the reviewed literature indicates that lowering baseline service demand and, in particular, peak requirements can reduce project counts and network upgrades, limit exposure to interconnection queues and permitting backlogs, ease upstream material pressures, and improve bankability under risk-averse finance. We conclude that net-zero pathways are more credible when service provision and demand-side design are treated as core planning variables alongside clean-energy supply expansion.
In order to investigate the impacts of government policy and market environment on the development of renewable energy, this paper constructs a computable general equilibrium (CGE) model to simulate the impacts of policy scenarios, market scenarios and policy-market scenarios on renewable energy output and investment, energy structure, economy and environment based on China’s input-output extension table in 2020. The results show that: the subsidy can optimize the energy structure and power structure, and non-hydropower renewable energy represented by wind power and solar power will become a new force of energy supply in China; the market environment of traditional energy price rising will increase the output and investment of renewable energy, and the energy policy can offset the negative impacts of traditional energy price downward on renewable energy; the impacts of subsidy on GDP, employment and emission reduction is positive; the GDP and employment is benefit from the market environment of traditional energy price downward, but the environment is damaged due to stimulating energy consumption. Finally, the government should support the renewable energy through energy policy, and monitor the market risk of the price fluctuation of traditional energy, so as to make the renewable energy become the key force of the transition of economic development mode and the focus of the breakthrough of energy environment dilemma. The novelty of this paper lies in simultaneously constructing policy scenarios, market scenarios, and integrated policy-market scenarios to examine the development of renewable energy and its corresponding economic and environmental impacts under each type of scenario, while also validating the effectiveness of energy policies in mitigating market risks.
The global energy transition toward net-zero emissions requires the massive development of renewable energy infrastructure. However, the efficiency and safety of installations such as wind turbines, solar panels, and energy storage systems are highly dependent on subsurface physical characteristics. Neglecting geological structures such as active faults or corrosive zones, risks structural failure and electrical system malfunctions. This research proposes the use of the Very Low Frequency Electromagnetic (VLF-EM) method as a fast and non-destructive geophysical screening tool. This method utilizes low-frequency signals to map variations in subsurface electrical conductivity. Furthermore, the data were processed using the Karous-Hjelt filter to identify fault structures and apparent current densities. Next, 2D inversion modeling was performed to visualize depth cross-sections and determine soil layer stability. The mapping results were classified into three risk zones that correlate directly with future energy needs. The yellow zone (stable) is a priority for heavy structure development (turbines/panels). The orange zone (transition) is an area for transmission lines and electric mobility. Meanwhile, the blue zone (conductive) represents areas to be avoided for heavy loads, but can be optimized as grounding points to protect the smart grid from lightning strikes. The targets and impacts of this research are cost efficiency, infrastructure safety, and sustainability. Based on the Karous-Hjelt filtering and 2D inversion images, area 4 is highly conductive, as indicated by the predominance of blue tones in this zone. Meanwhile, area 3 is the most resistive, marked by a predominance of yellow and orange. In terms of cost efficiency, this approach reduces initial geotechnical survey costs through electromagnetic screening methods. For infrastructure safety, it minimizes the risk of damage to sensitive electronic devices in electric vehicles and energy storage systems through grounding system optimization. Regarding sustainability, this provides a roadmap for energy developers to select the most geophysically stable and safe locations in Indonesia. This research demonstrates that the synergy between electromagnetic waves and energy technology is the key to creating an energy ecosystem that is not only clean but also safe for subsurface structures.
This paper evaluates the role of aggregated demand-side flexibility from public buildings in supporting renewable energy integration and decarbonization of the Slovenian energy system. Using high-resolution monitoring data from over 100 public buildings and the H2RES energy system optimization model, two long-term scenarios are analyzed for the period 2020–2050: a reference scenario without public building flexibility and a flexibility scenario in which public building heat demand is electrified via heat pumps. The year 2020 is used as an internally consistent optimization reference for demand levels and technology availability, rather than as a statistical reconstruction of the observed national energy system. Reported CO2 emissions represent modelled energy-system emissions within the covered sectors (power, heat, and industry) under exogenously imposed policy constraints and therefore may differ from national inventory totals. The results show that aggregated public building flexibility affects the temporal allocation of electricity demand and sectoral emission distribution, leading to moderate changes in renewable curtailment and heat-sector CO2 emissions while maintaining identical renewable energy targets. The findings demonstrate that electrification and aggregation of public building demand can play a strategic role in supporting national climate objectives and system operation under high renewable penetration. It should be noted that the analyzed dataset, while based on high-resolution monitoring of more than 100 public buildings, is not statistically scaled to represent the full national building stock. The results should therefore be interpreted as indicative of system-level effects of aggregated flexibility rather than a direct national-scale quantification.
Accurate building electricity load forecasting (BELF) can provide a regulatory basis for building energy management systems and promote the transition of buildings toward low-carbon and intelligent operation modes. However, building electricity load is influenced by historical loads, as well as outside environmental conditions such as humidity and temperature, which reduces the prediction accuracy of models. To tackle these challenges, this study presents a BELF model, which consists of a modal component grouping approach, grouped feature attention mechanism, and multi-scale residual depthwise convolution memory module. First, the modal component grouping method analyzes building electricity load in the time domain, frequency domain (via fast fourier transform, FFT), and complexity (via sample entropy, SE), and then performs clustering to achieve precise decomposition of load components with different fluctuation characteristics. Second, the grouped feature attention mechanism assigns suitable importance to various input features to emphasize key factors affecting prediction accuracy. Third, the multi-scale residual depthwise convolution memory module mitigates the impact of long and short-term load variations on BELF by employing residual blocks of depthwise convolution layers with different kernel sizes. Meanwhile, gated recurrent units are used to identify the time-dependent trends of building load. Experimental results on public buildings show that the proposed model outperforms existing models, achieving more than 2.4% improvement in MAPE prediction performance.
This study numerically investigates turbulent flow and thermal performance in evaporator tubes equipped with rectangular partitions positioned at different locations. Two configurations are analyzed: (A) partitions on the top wall, center of channel, and bottom wall, and (B) partitions on the bottom wall, center of channel, and top wall. In addition, we examine the effect of varying the positions of the obstacles (S=D2,S=D,S=5D4, andS=3D/2) and the inclination angle (θ=60∘, θ=75∘, θ=90∘, θ=105∘ and θ=120∘) of the detached obstacle relative to the walls, an innovative aspect that had not been addressed in previous studies. Using computational fluid dynamics (CFD), heat transfer and hydrodynamic behavior are evaluated under steady-state conditions for Reynolds numbers ranging from 10,000 to 30,000. Results show that configuration A enhances dynamic pressure and Nusselt number while reducing friction, yielding a thermal performance enhancement factor (TEF) greater than 1 across all cases. The originality of this work lies in the comparative evaluation of partition positioning and inclination, demonstrating that optimized geometries can significantly improve heat transfer efficiency while minimizing flow resistance compared to conventional evaporators.
