Strategic distribution network planning with smart grid technologies

This paper presents a multiyear distribution network planning optimization model for managing the operation and capacity of distribution systems with significant penetration of distributed generation (DG). The model considers investment in both traditional network and smart grid technologies, including dynamic line rating, quadrature-booster, and active network management, while optimizing the settings of network control devices and, if necessary, the curtailment of DG output taking into account its network access arrangement (firm or non-firm). A set of studies on a 33 kV real distribution network in the U.K. has been carried out to test the model. The main objective of the studies is to evaluate and compare the performance of different investment approaches, i.e., incremental and strategic investment. The studies also demonstrate the ability of the model to determine the optimal DG connection points to reduce the overall system cost. The results of the studies are discussed in this paper

Stochastic optimisation-based valuation of smart grid options under firm DG contracts

Under the current EU legislation, Distribution Network Operators (DNOs) are expected to provide firm connections to new DG, whose penetration is set to increase worldwide creating the need for significant investments to enhance network capacity. However, the uncertainty around the magnitude, location and timing of future DG capacity renders planners unable to accurately determine in advance where network violations may occur. Hence, conventional network reinforcements run the risk of asset stranding, leading to increased integration costs. A novel stochastic planning model is proposed that includes generalized formulations for investment in conventional and smart grid assets such as Demand-Side Response (DSR), Coordinated Voltage Control (CVC) and Soft Open Point (SOP) allowing the quantification of their option value. We also show that deterministic planning approaches may underestimate or completely ignore smart technologies

Genetic and Phenotypic Variability for Racing Performance of Trotter Horse in Serbia

Horse race results in studbooks are supposed to give information to manage the selection of trotters based on the analysis of genetic and phenotypic parameters. The objectives of this study was to estimate genetic and phenotypic variability of three racing traits (number of starts, race time and best racing time) of trotter horses in Serbia. The data were obtained from the Trotting Association of Serbia and consisted of 2252 observations. The model included effect of sex, year of birth, season, year of race, distance and race track as fixed effects and sire as random effect. The BLUP sire model was applied to the genetic evaluation of measured traits. Average mean of number of starts, race time and best racing time was 64, 83.15 and 79.28, respectively. Of all tested fixed effects only distance was not statistically significant for number of starts and season for best racing time. However, a statistically highly significant influence of all tested fixed effects on racing time was shown. Heritability estimates were 0.28 for number of starts, 0.19 for racing time and 0.35 for best racing time. The low heritability estimates for number of starts and racing time indicate that selection based on horse phenotypic value induces small genetic change in these traits while middle level of heritability for best racing time indicates that animal's phenotype is a good indicator of genetic merit or breeding value

Application of liquid-air and pumped-thermal electricity storage systems in low-carbon electricity systems

In this study, w e consider two medium - to large - scale electricity storage systems currently under development, namely ‘Liquid - Air Energy Storage’ (LAES) and ‘Pumped - Therma l Electricity Storage’ (PTES). Consistent t hermodynamic models and costing methodologies for the two systems are presented , with the object ive of integrating the characteristics of these technologies in to a whole - electricity system assessment model , and assess ing the ir system - level value in different scenarios for power system decarbonisation . It is found that the value of storage varies greatly depending on the cumulative installed ca pacity of storage in the electrical system, with the s torage technologies provid ing greater marginal benefits at low p enetrations . T wo carbon target scenarios showed similar results, with a limited effect of the carbon target on the system value of storage (although it is noted that this may change for even more ambitious carbon targets). On the other hand, the location and installed capacity of storage plants is found to have a significant impact on the syste m value and acceptable cost of the se technologies. The w hole - system value of PTES was found to be slightly higher than that of LAES, driven by a higher storage duration and efficiency, however, due to the higher power capital cost of PTES, this becomes les s attractive for implementat ion at lower volumes than LAES

Robust and automatic data cleansing method for short-term load forecasting of distribution feeders

Distribution networks are undergoing fundamental changes at medium voltage level. To support growing planning and control decision-making, the need for large numbers of short-term load forecasts has emerged. Data-driven modelling of medium voltage feeders can be affected by (1) data quality issues, namely, large gross errors and missing observations (2) the presence of structural breaks in the data due to occasional network reconfiguration and load transfers. The present work investigates and reports on the effects of advanced data cleansing techniques on forecast accuracy. A hybrid framework to detect and remove outliers in large datasets is proposed; this automatic procedure combines the Tukey labelling rule and the binary segmentation algorithm to cleanse data more efficiently, it is fast and easy to implement. Various approaches for missing value imputation are investigated, including unconditional mean, Hot Deck via k-nearest neighbour and Kalman smoothing. A combination of the automatic detection/removal of outliers and the imputation methods mentioned above are implemented to cleanse time series of 342 medium-voltage feeders. A nested rolling-origin-validation technique is used to evaluate the feed-forward deep neural network models. The proposed data cleansing framework efficiently removes outliers from the data, and the accuracy of forecasts is improved. It is found that Hot Deck (k-NN) imputation performs best in balancing the bias-variance trade-off for short-term forecasting

Sources of Variation for Milk Traits in Regions of Vojvodina

The study aimed to investigate different sources of variation for milk traits in dairy cows, in first lactation from three breeding regions of Vojvodina (Srem, Banat and Backa). For research purposes a total of 2767 complete and 305 days records of milk yield (MY), fat yield (FY) and milk fat content (MFC) of Holstein-Friesian (HF, black and red) dairy cows was used. All cows were involved in the official milk recording program in 2013 in Vojvodina. Milk traits were analyzed using the mixed linear model in order to explain total variation with bull-sire as a random effect, region, year of birth and calving season as fixed effects and length of lactation as covariates. The average values of MY, MF and MFC in the first lactation of 305 days were 6053.4 kg of milk, 225.24 kg of milk fat and 3.74% milk fat content. The effects of the bull-sire, calving season, year of birth and breeding region on all investigated milk traits were highly significant (p>0.01) during 305 days, but year of birth for complete records had no significant effect on these parameters (P>0.05)

Long-Term Expansion Planning of the Transmission Network in India under Multi-Dimensional Uncertainty

Considerable investment in India’s electricity system may be required in the coming decades in order to help accommodate the expected increase of renewables capacity as part of the country’s commitment to decarbonize its energy sector. In addition, electricity demand is geared to significantly increase due to the ongoing electrification of the transport sector, the growing population, and the improving economy. However, the multi-dimensional uncertainty surrounding these aspects gives rise to the prospect of stranded investments and underutilized network assets, rendering investment decision making challenging for network planners. In this work, a stochastic optimization model is applied to the transmission network in India to identify the optimal expansion strategy in the period from 2020 until 2060, considering conventional network reinforcements as well as energy storage investments. An advanced Nested Benders decomposition algorithm was used to overcome the complexity of the multistage stochastic optimization problem. The model additionally considers the uncertainty around the future investment cost of energy storage. The case study shows that deployment of energy storage is expected on a wide scale across India as it provides a range of benefits, including strategic investment flexibility and increased output from renewables, thereby reducing total expected system costs; this economic benefit of planning with energy storage under uncertainty is quantified as Option Value and is found to be in excess of GBP 12.9 bn. The key message of this work is that under potential high integration of wind and solar in India, there is significant economic benefit associated with the wide-scale deployment of storage in the system

On machine learning-based techniques for future sustainable and resilient energy systems

Permanently increasing penetration of converter-interfaced generation and renewable energy sources (RESs) makes modern electrical power systems more vulnerable to low probability and high impact events, such as extreme weather, which could lead to severe contingencies, even blackouts. These contingencies can be further propagated to neighboring energy systems over coupling components/technologies and consequently negatively influence the entire multi-energy system (MES) (such as gas, heating and electricity) operation and its resilience. In recent years, machine learning-based techniques (MLBTs) have been intensively applied to solve various power system problems, including system planning, or security and reliability assessment. This paper aims to review MES resilience quantification methods and the application of MLBTs to assess the resilience level of future sustainable energy systems. The open research questions are identified and discussed, whereas the future research directions are identified