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
This paper presents a multi-temporal approach for the energy scheduling and voltage/var control problem in a microgrid (MG) system with photovoltaic (PV) generation and energy storage devices (PV-battery MG) during islanded operation conditions. A MG is often defined as a low voltage (LV) distribution grid that encompasses distributed energy resources and loads that operate in a coordinated way, either connected to the upstream distribution grid or autonomously (islanded from the main grid). Considering the islanded operation of the MG during a given period, it is necessary to develop proper tools that allow the effective coordination of the existing resources. Such tools should be incorporated in the MG control system hierarchy in order to assure proper conditions for the operation of the autonomous MG in terms of active power, voltage and reactive power management. Energy storage devices are essential components for the successful operation of islanded MG. These devices have a very fast response and are able to absorb/inject the right amount of power. For the operation of the MG in islanding conditions during a longer period, it is necessary to integrate information related to the forecasting of loads and PV-based generation for the upcoming hours for which is intended to maintain MG in islanded operation. Therefore, this paper presents a tool to be integrated in the Microgrid Central Controller (MGCC) that is responsible to perform a multi-temporal optimal power flow (OPF) in order to schedule the active and reactive power within the MG for the next time intervals. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Abstract
The restructuring of power systems induced new challenges to generation companies in terms of adequately planning the operation of power stations in order to maximize their profits. In this scope, hydro resources are becoming extremely valuable given the revenues that their operation can generate. In this paper we describe the application of the Matlab (R) Linprog optimization function to solve the Short Term Hydro Scheduling Problem, HSP, admitting that some stations are installed in the same cascade and that some of them have pumping capabilities. The optimization module to solve the HSP problem is then integrated in an iterative process to take into account the impact that the operation decisions regarding the hydro stations under analysis have on the market prices. The updated market prices are then used to run again the HSP problem thus enabling considering the hydro stations as price makers. The developed approach is illustrated using a system based on the Portuguese Douro River cascade that includes 9 hydro stations (4 of them are pumping stations) and a total installed capacity of 1485 MW.