Energy management systems for the operation of distributed energy resources, distribution storage devices, and responsive loads will be embedded in distribution management systems (DMSs) as advanced functions that rely on accurate input data and fast communication signals. For a proper DMS design, the impact of the state estimation uncertainties and of the communication system delays should be evaluated. The paper presents an integrated software package for the cyber-physical simulation of DMS taking into account the accuracy of state estimation and measurements. The effectiveness of a wireless Wi-Max communication system has been tested on the rural Italian representative network produced by the research project ATLANTIDE. Results proved the need of cosimulation packages in modern distribution systems.

The challenge for EU member states is to ensure the timely evolution of DNOs to their future, more active roles, which are necessary to make low-carbon societies a reality. While the corresponding regulatory actions will largely depend on the context and characteristics of the distribution networks within each member state, it is clear that incentivizing innovation should be key in this endeavor. Equally critical is defining the roles of potential future players and technologies in the provision of flexibility (e.g., consumers, aggregators, and storage) to prevent new business models being hindered by regulatory barriers. This transition of DNOs to DSOs in the next few years will certainly bring exciting new regulatory environments where the envisioned smart grids are likely to finally emerge.

Energy storage systems can improve the uncertainty and variability related to renewable energy sources such as wind and solar create in power systems. Aside from applications such as frequency regulation, time-based arbitrage, or the provision of the reserve, where the placement of storage devices is not particularly significant, distributed storage could also be used to improve congestions in the distribution networks. In such cases, the optimal placement of this distributed storage is vital for making a cost-effective investment. Furthermore, the now reached massive spread of distributed renewable energy resources in distribution systems, intrinsically uncertain and non-programmable, together with the new trends in the electric demand, often unpredictable, require a paradigm change in grid planning for properly lead with the uncertainty sources and the distribution system operators (DSO) should learn to support such change. This paper considers the DSO perspective by proposing a methodology for energy storage placement in the distribution networks in which robust optimization accommodates system uncertainty. The proposed method calls for the use of a multi-period convex AC-optimal power flow (AC-OPF), ensuring a reliable planning solution. Wind, photovoltaic (PV), and load uncertainties are modeled as symmetric and bounded variables with the flexibility to modulate the robustness of the model. A case study based on real distribution network information allows the illustration and discussion of the properties of the model. An important observation is that the method enables the system operator to integrate energy storage devices by fine-tuning the level of robustness it willing to consider, and that is incremental with the level of protection. However, the algorithm grows more complex as the system robustness increases and, thus, it requires higher computational effort.

The interconnection of large amounts of non-traditional generation may cause problems to networks designed for 'conventional' operation. A Microgrid consists of a combination of generation sources, loads and energy storage interfaced through fast acting power electronics. The aim of operating microgrid sub-systems is to move away from considering DG as badly behaved system components, of which a limited amount can be tolerated in an area, to 'good citizens'. The paper aims at assessing the economic benefits achievable by a group of industrial and commercial customers aggregated in a Microgrid controlled with a central controller that uses a neural network to optimise the schedule of generators and responsive loads. The central controller receives market signals, load and generation bids, load and weather forecasts and determines hour by hour the correct dispatch of generators to maximise the value of the microgrid by minimising the energy costs.

Many planning and operation studies that aim at fully assessing and optimizing the performance of the distribution grids, in response to the current trends, cannot ignore grid limitations. Modelling the distribution system, by including the electrical characteristics of the network (e.g., topology) and end user behaviors, has become complex, but essential, for all conventional and emerging actors/players of power systems (i.e., system and market operators, regulators, new market parties as service providers, aggregators, researchers, etc.). This paper deals with a methodology that, starting from publicly available open data on the energy consumption of a region or wider area, is capable to obtain reasonable load and generation profiles for the network supplied by each primary substation in the region/area. Furthermore, by combining these profiles with territorial and socio-economic information, the proposed methodology is able to model the network in terms of lines, conductors, loads and generators. The results of this procedure are the synthetic networks of the real distribution networks, that do not correspond exactly to the actual networks, but can characterize them in a realistic way. Such models can be used for all the kind of optimization studies that need to check the grid limitations. Results derived from Italian test cases are presented and discussed.