Abstract
This study proposes a hierarchical optimisation strategy for the energy dispatch and volt/var control problem of a photovoltaic-battery microgrid cluster (MGC) operating autonomously. The proposed approach takes advantage of the decentralised control architecture existing in multi-microgrids (MMGs) framework by distributing the management responsibilities between the microgrid central controllers (MGCCs) and the central autonomous management controller (CAMC). In the first stage, the optimisation strategy solves a multi-temporal active power scheduling problem for the MGC based on consumption and generation forecasts. In the second stage, the reactive power and volt/var control are addressed by taking into account the medium-voltage (MV) and low-voltage levels independently. For this purpose, each MGCC computes the V(Q) capability area of operation at the boundary bus with the MV grid. Then, the CAMC performs an optimal power flow at the MV level for each time step, whose results at the boundary bus are considered in the last stage to schedule reactive power at the MGCC level. The effectiveness of the proposed strategy is demonstrated in a cluster of three microgrids. It keeps the modularity, interoperability and scalability characteristics of the MMG concept by clearly defining the roles and the information to be exchanged between the CAMC and the MGCC.

Abstract
Large horsepower induction motors play a critical role in the operation of industrial facilities. In this respect, the distribution network operators dedicate a high priority to the operational safety of these motor loads. In this paper, the induction motor starting is modeled analytically and in a semi-static fashion. This model is imbedded in a convex distribution system restoration problem. In this optimization problem, it is aimed to determine the optimal status of static loads and the optimal dispatch of distributed generators such that: a) the induction motors can be reaccelerated in a safe way and, b) the total power of static loads that cannot be supplied before the motor energization, is minimized. The proposed optimization problem is applied in the case of a distribution network under different simulation scenarios. The feasibility and accuracy of the obtained results are validated using a) off-line time-domain simulations, and b) Power Hardware-In-the-Loop experiments.

Abstract

Abstract
Forecasting for wind and solar renewable energy is becoming more important as the amount of energy generated from these sources increases. Forecast skill is improving, but so too is the way forecasts are being used. In this paper, we present a brief overview of the state-of-the-art of forecasting wind and solar energy. We describe approaches in statistical and physical modeling for time scales from minutes to days ahead, for both deterministic and probabilistic forecasting. Our focus changes then to consider the future of forecasting for renewable energy. We discuss recent advances which show potential for great improvement in forecast skill. Beyond the forecast itself, we consider new products which will be required to aid decision making subject to risk constraints. Future forecast products will need to include probabilistic information, but deliver it in a way tailored to the end user and their specific decision making problems. Businesses operating in this sector may see a change in business models as more people compete in this space, with different combinations of skills, data and modeling being required for different products. The transaction of data itself may change with the adoption of blockchain technology, which could allow providers and end users to interact in a trusted, yet decentralized way. Finally, we discuss new industry requirements and challenges for scenarios with high amounts of renewable energy. New forecasting products have the potential to model the impact of renewables on the power system, and aid dispatch tools in guaranteeing system security. This article is categorized under: Energy Infrastructure > Systems and Infrastructure Wind Power > Systems and Infrastructure Photovoltaics > Systems and Infrastructure

Abstract
This paper presents a study on dynamic line rating (DLR) forecasting procedure aimed at developing a new methodology able to forecast future ampacity values for rare and extreme events. This is motivated by the belief that to apply DLR network operators must be able to forecast their values and this must be based on conservative approaches able to guarantee the safe operation of the network. The proposed methodology can be summarised as follows: firstly, probabilistic forecasts of conductors' ampacity are calculated with a non-parametric model, secondly, the lower part of the distribution is replaced with a new distribution calculated with a parametric model. The paper presents also an evaluation of the proposed methodology in network operation, suggesting an application method and highlighting the advantages. The proposed forecasting methodology delivers a high improvement of the lowest quantiles' reliability, allowing perfect reliability for the 1% quantile and a reduction of roughly 75% in overconfidence for the 0.1% quantile.

Abstract
The current coordination between the transmission system operator (TSO) and the distribution system operator (DSO) is changing mainly due to the continuous integration of distributed energy resources (DER) in the distribution system. The DER technologies are able to provide reactive power services helping the DSOs and TSOs in the network operation. This paper follows this trend by proposing a methodology for the reactive power management by the DSO under renewable energy sources (RES) forecast uncertainty, allowing the DSO to coordinate and supply reactive power services to the TSO. The proposed methodology entails the use of a stochastic AC-OPF, ensuring reliable solutions for the DSO. RES forecast uncertainty is modeled by a set of probabilistic spatiotemporal trajectories. A 37-bus distribution grid considering realistic generation and consumption data is used to validate the proposed methodology. An important conclusion is that the methodology allows the DSO to leverage the DER full capabilities to provide a new service to the TSO.