Andrea Michiorri

Investigation into the influence of environmental conditions on power system ratings

Abstract This article presents research that seeks to assist distribution network operators in the adoption of real-time thermal rating (RTR) systems. The exploitation of power system rating variations is challenging because of the complex nature of environmental conditions such as wind speed. The adoption of an RTR system may overcome this challenge and offers perceived benefits such as increased distributed generation (DG) accommodation and avoidance of component damage or premature ageing. Simulations, using lumped parameter component models, are used to investigate the influence of environmental conditions on overhead line, electric cable, and power transformer ratings. Key findings showed that the average rating of overhead lines, electric cables, and power transformers ranged from 1.70 to 2.53, 1.00 to 1.06, and 1.06 to 1.10 times the static rating, respectively. Since overhead lines were found to have the greatest potential for rating exploitation, the influence of environmental conditions on four overhead line types was investigated and it was shown that the value of an RTR system is location dependent. Furthermore, the additional annual energy yield from DG that could potentially be accommodated through deployment of an RTR system was found to be 54 per cent for the case considered.

Overhead line real-time rating estimation algorithm: Description and validation

Abstract This article describes research that aims to realize a real-time rating (RTR) system for power system components. The RTR technology is regarded with interest due to its potential to unlock network power transfer capacity, improve power flow congestion management flexibi-lity, and facilitate the connection of distributed generation. The solution described in this work involves the use of a limited number of meteorological stations and a series of analytical models for estimating component ratings. The effect of data uncertainty is taken into account by an estimation algorithm based on the Monte Carlo method. Estimations of conductor temperature and environmental conditions have been validated against measured data in five different network locations. Average errors of-2.2,-1.9,-1.2,-1.9, and 1.4 °C were found for the five different network locations over a period of 71 days when comparing estimates to measured results. Results analysis identified that the models used were the main source of error. The estimation of wind direction and solar radiation was the most sensitive to errors in the models. Therefore, suggestions are made regarding the improvement of these models and the RTR estimation system.

A local energy management system for solar integration and improved security of supply: The Nice Grid project

This paper describes Nice Grid, a demonstration project part of the European initiative Grid4EU. The project aims at developing a smart solar neighbourhood in the urban area of the city of Nice, France. The four year project started in November 2011 and will test the suitability of recent developments in distribution networks management for facilitating the connection of distributed renewable generators, improving the security of supply and let customers and other actors to provide network services. The idea behind Nice Grid is to combine controllable distributed electricity and thermal storage devices with forecasts of solar power production and load in a local energy management system. The paper, which represents a useful reference for the project, presents also a detailed overview of relevant European demonstration projects on Smart Grid.

The value of schedule update frequency on distributed energy storage performance in renewable energy integration

This paper describes preliminary findings of research on the use of Distributed Energy Storage devices for Renewable Energy integration. The primary objective is to describe the effect of different storage scheduling strategies, and namely the benefits from intraday intraday scheduling on the storage performance in renewable energy integration. Optimal schedules of Distributed Energy Storage devices are based on forecasts of Renewable Energy production, local consumption and prices, along with other criteria. These forecasts tend to have a higher uncertainty for higher time horizons, resulting in losses due to errors and to the underutilization of the assets. The use of frequent schedules updates can reduce part of these drawbacks and this paper aims at quantifying this reduction. The importance of the quantification of the benefits arising from different rescheduling frequencies lies in its influence on the ICT infrastructure necessary to implement it and its cost.

Impact of PV forecasts uncertainty in batteries management in microgrids

This paper is motivated by the question of the impact that uncertainty in PV forecasts has in forecast-based battery schedule optimisation in microgrids in presence of network constraints. We examine a specific case where forecast accuracy can be impacted by the lack of enough data history to finetune the forecasting models. This situation can be expected to be frequent with new PV installations. A probabilistic PV production forecast algorithm is used in combination with a battery schedule optimisation algorithm. The size of the learning dataset of the forecast algorithm is modified in order to simulate the application of the system to new plants and the impact on the performance in the management of the battery is analysed.

Understanding the Benefits of Dynamic Line Rating Under Multiple Sources of Uncertainty

This paper analyzes the benefits of dynamic line rating (DLR) in the system with high penetration of wind generation. A probabilistic forecasting model for the line ratings is incorporated into a two-stage stochastic optimization model. The scheduling model, for the first time, considers the uncertainty associated with wind generation, line ratings, and line outages to co-optimize the energy production and reserve holding levels in the scheduling stage as well as the redispatch actions in the real-time operation stage. Therefore, the benefits of higher utilization of line capacity can be explicitly balanced against the costs of increased holding and utilization of reserve services due to the forecasting error. The computational burden driven by the modeling of multiple sources of uncertainty is tackled by applying an efficient filtering approach. The case studies demonstrate the benefits of the DLR in supporting cost-effective integration of high penetration of wind generation into the existing network. We also highlight the importance of simultaneously considering the multiple sources of uncertainty in understanding the benefits of DLR. Furthermore, this paper analyzes the impact of different operational strategies, the coordination among multiple flexible technologies, and the installed capacity of wind generation on the benefits of DLR.

Optimal scheduling of storage devices in smart buildings including battery cycling

This paper presents an optimization model for energy management in smart buildings, when electrochemical and thermal storage are considered as flexibilities to achieve minimum operation costs. The optimization problem takes into account the batterys cycling cost and the possibility of storing energy in the electric water heater. To deal with the cycling aging process, the problem is decomposed into two subproblems that are iteratively solved, in which a Particle Swarm Optimization decides the batterys State of Charge and then a day-ahead dispatch takes place to determine the total operation cost. This approach allows us to deal with the non-linearities of battery aging in a simple an effective way. The results show that the potential presence of both storage technologies has a positive impact on the operation costs; they also show the impact on the device settings when batterys cycling aging cost is considered. This methodology has been developed in the context of the Horizon 2020 project SENSIBLE as part of the tasks related to the use case, Flexibility and Demand Side Management in Market Participation.