The hosting capacity of low-voltage (LV) networks is influenced by existing consumption and production in adjacent LV networks under the same medium-voltage (MV) network. Since LV transformers typically lack automatic on-load tap changers, both voltage and current limits require a joint assessment covering the MV network and all underlying LV networks. This paper introduces a methodology to include different MV operating conditions, like reserve operating paths, in the calculation of the hosting capacity for new production at LV networks. The MV voltage profile before the connection of new production, called the background voltage, is modelled to enable such analysis. The methodology is applied to an existing MV/LV network. The hosting capacity was lower, for the studied reserve operating paths, and the limitation was due to overvoltage issues. The proposed methodology is a valuable tool for distribution network planning. It was also shown that it is essential to use an accurate model of the background voltage for hosting capacity calculation of distribution networks.

Solar PV connections at medium voltage (MV) level can impact low voltage (LV) networks by reducing their margin for installing additional PV units. This paper presents an approach to estimate the hosting capacity of distribution networks, including an integrated model of MV-LV networks and a visualisation method for assessing the sharing of hosting capacity between voltage levels. The objective is to quantify the mutual influence of MV and LV connections and to support the planning and management of connection requests. The proposed visualisation method illustrates the trade-off between MV and LV production units, accounting for both voltage and loading limitations, and provides a good representation of how PV connections at one voltage level affect the other. Results show that the coupling between MV and LV networks significantly impacts the hosting capacity, how it can be shared, and that constraints at either level can limit future connections. The method facilitates the identification of the voltage level imposing the most significant limitations and thus also supports the assessment of where and which mitigation strategies would be more effective. Overall, the method highlights the importance of a joint assessment considering the MV-LV coupling to support informed planning decisions. 

The term “hosting capacity”, in the context of power systems, was first introduced in March 2004 and has since resulted in a range of applications and research. Network operators commonly use the concept to quantify the ability of their networks to accept new production and consumption. Academic research on hosting capacity took off seriously after 2010 and has resulted in thousands of publications. This paper presents a brief history of the early years of hosting capacity studies, gives an overview of the status of both applications and research, summarises the different methods and types of studies, and correlates all that with the underlying fundamental principles of the hosting capacity concept, as it was introduced in 2004. The main focus of this paper is to review and relate these methods and studies to the fundamental principles. Having a clear understanding of these fundamental principles enables a wide range of applications for hosting capacity studies with detailed methods and models. However, it also requires transparency to ensure a coherent analysis, correct interpretation of the results, and an open discussion between stakeholders. With research on hosting capacity, it is important to refer to the fundamental principles; with applications, it is important to maintain transparency and objectivity. 

This paper presents an approach to estimate the hosting capacity for distribution networks considering the impact of PV penetration at different voltage levels. The estimation and the method were selected such that the results were most suitable for distribution system planning. A time-series based method was used as it covers significant aspects needed for prioritising network reinforcement. The MV background voltage was modelled varying in time, assuming the same penetration level in the other LV networks supplied by the same MV system. The hosting capacity is defined as the maximum acceptable PV size per customer for a given PV penetration. Based on the different possible combinations of PV location, the probability of overvoltage and overloading is used as a performance index. The planning risk is used as a limit for the performance criterion. The method can be automated for a large number of networks due to using an IEC 61970-based input format. It also enables linking DSO network models to customer smart metre databases. The severity and risks of limit violations are analysed with different metrics from the time-series simulations. The change in background voltage with increasing penetration is shown to impact the results significantly. When considering it, the estimated hosting capacity was reduced by 32 %, on average.

This paper addresses the critical challenge of reducing dependence on multiple physical sensors in photovoltaic energy systems, which often leads to increased cost, system complexity, and vulnerability to noise and sensor failures. These issues impact the overall reliability and real-time performance of power extraction and motivate the development of more robust and efficient alternatives. As a solution, an adaptive software sensor is introduced and integrated with a smart maximum power point tracking control strategy for real-time photovoltaic system optimization. The proposed software sensor is designed using an adaptive super-twisting sliding mode observer to estimate internal states, such as inductance current and output voltage. The control strategy is based on artificial neural networks combined with sliding mode control to ensure accurate and stable power tracking under varying environmental conditions. The software sensor’s parameters are automatically adapted in real time to maintain estimation accuracy and robustness. Convergence of the proposed method is analytically verified using Lyapunov theory. Simulation results based on real-world solar irradiance data demonstrate high performance, achieving 99.9% power efficiency and a 4.64% improvement in estimation accuracy compared to the conventional super-twisting observer. These findings confirm the effectiveness of the proposed architecture in enhancing photovoltaic system operation. 

Congestion in the transmission network induces price spread between bidding zones in the electricity market. The price spread is the difference between the zone price with congestion and the zonal price without congestion. The latter, the so-called "uniform price", is calculated as the intersection of the aggregated demand and supply curves for the whole country. In this paper, the price spreads for the different bidding zones in Sweden are calculated over a year to analyze the impact of congestion on the electricity market. Price spread results show that the day-ahead market was cleared without congestion between four bidding zones, during about 60% of the hours. When congestion occurs, SE1 and SE2 are more likely to have a zonal price below the uniform price, and SE3 and SE4 have a higher zonal price than the uniform price. If the price spread is negative, the impact is calculated by multiplying the price spread by the zonal power generation. If the price spread is positive, it is multiplied by the consumption to calculate the impact. The numerical outcomes from the impact calculation reveal that there is a relatively small risk for producers in the SE1 and SE2 bidding zones and a much larger risk for consumers in the SE3 and SE4 bidding zones.

Digital substation technology adhering to the IEC 61850 standard has provided several opportunities and flexibility for the rapid growth and complexity of the present and future electrical grid. The communication infrastructure allows complete interoperability between legacy and modern devices. The emergence of 5G wireless communication and its utilization in substation operation presents significant advantages in terms of cost and scalability, while also introducing challenges. This paper identifies research gaps in the literature and offers valuable insights for future analysis by providing a simulation study using an empirical latency dataset of a 5G network to illustrate three aspects of substation operational challenges: coordination of protection schemes, sequential reception of packet data streams, and time synchronization processes. The findings show a mean latency of 8.5 ms for the 5G network, which is significantly higher than that of a wired Ethernet network. The results also indicate that the high latency and jitter compromise the selectivity of protection schemes. The variability in latency disrupts the sequence of arriving data packets such that the packet buffering and processing delay increases from around 1.5 ms to 11.0 ms and the buffer size would need to increase by 6 to 10 times to handle out-of-sequence packets. Additionally, a time synchronization success rate of 14.3% within a 0.1 ms accuracy range found in this study indicates that the IEEE 1588 protocol is severely affected by the latency fluctuations.

Supraharmonics are present in low-voltage (LV) networks, and they can cause interference with equipment and power line communication. The propagation of supraharmonics is defined by the network impedance which in turn depends on the impedance of the devices connected to the network. The probability of interference due to supraharmonics is then dependent on the customers’ behavior. This article presents a case study for the evaluation of the probability of interference considering the uncertainty in LV customers’ supraharmonic emission and impedance. A stochastic analysis of the system characteristics and supraharmonic emission is used. The results for the case study presented in the paper show that there could be a 40% probability of surpassing the compatibility levels in certain nodes which translates into a risk of interference due to supraharmonics. The simulation results are supported by measurements performed in a laboratory, which can emulate a network like the simulated one. The simulation and measurements show that in the frequency range of 27 kHz to 150 kHz the compatibility levels are surpassed.

Industrial installations are often highly sensitive to voltage dips. Traditional analysis of voltage dips, defined by reduced voltage over a short duration, usually does not reveal all the details of the impact on connected devices. This study focuses on detailed voltage dip modelling, particularly the transients at beginning and ending of the dip. The propagation and amplification of these transients have been analyzed for four fault types in a transmission system. Results indicate significant transient amplification in the industrial distribution grid, especially during three-phase-to-ground faults when the on-site generator is off. The impact of voltage dip transients was assessed for common industrial devices, induction motor, diode rectifier, and voltage source converter. These findings provide valuable insights into voltage dip transients and their effects on low-voltage devices, supporting the need for further studies into this phenomenon.

Operational risk assessment is a stochastic approach to power system planning with a lead time of hours to days starting from an observation instant. Operational risk assessment requires component state probabilities as a function of time. The commonly used two-state component outage model does not include protection failures. Two challenges occur when including these: estimating the probability of hidden failure states at the observation instant and estimating time-dependent state probabilities. Both challenges are addressed in this paper. By deploying a Markov model with average transition rates, the probability of the hidden failure states at the observation instant is estimated. From this initial probability and a Markov model with transition rates valid during the lead time, the state probabilities as a function of time are obtained. The Taylor expansion is used to obtain linear, square, or higher-order approximations of the state probabilities as a function of time. The method is illustrated using two case studies: a 4-state component model and a 15-state component model; different levels of adverse weather during the lead time are used.