Among the innovative contributions to electric distribution systems, one of the most promising and qualified is the possibility to manage and control distributed generation. Therefore, the latest distribution management systems tend to incorporate optimization functions for the short-term scheduling of the various energy and control resources available in the network (e.g., embedded generators, reactive power compensators and transformers equipped with on-load tap changers). The short-term scheduling procedure adopted in the paper is composed by two stages: a day-ahead scheduler for the optimization of distributed resources production during the following day, an intra-day scheduler that every 15 min adjusts the scheduling in order to take into account the operation requirements and constraints of the distribution network. The intra-day scheduler solves a non-linear multi-objective optimization problem by iteratively applying a mixed-integer linear programming (MILP) algorithm. The linearization of the optimization function and the constraints is achieved by the use of sensitivity coefficients obtained from the results of a three-phase power flow calculation. The paper shows the application of the proposed approach to a medium-voltage 120 buses network with five wind plants, one photovoltaic field, ten dispatchable generators, and two transformers equipped with on-load tap changers.

The paper proposes the modeling and the optimal management of a hot-temperature (sodium nickel chloride) battery system coupled with wind generators connected to a medium voltage grid. A discrete-time model of the storage device reproducing the battery main dynamics (i.e., state of charge, temperature, current, protection, and limitation systems) has been developed. The model has been validated through some experimental tests. An optimal management strategy has been implemented based on a forward dynamic programming algorithm, specifically developed to exploit the energy price arbitrage along the optimization time horizon (“generation shifting”). Taking advantage of this strategy wind generation performances can be enhanced and adapted to load demand, obtaining an increased economic gain measured by the difference between the economic revenue obtained with and without the proposed generation shifting policy.

In the context of power system restructuring, the maintaining of adequate levels of security and reliability will be operated also through direct load control, thus considering load as the potential provider of ancillary services such as regulation, load-following, frequency responsive spinning reserve. In any case load-shedding still remains the ultimate resource for emergency conditions. In the paper several load-shedding schemes for underfrequency operation are examined. Both traditional schemes, based only on frequency thresholds, and adaptive schemes, based on frequency and on its rate of change, are considered. An IEEE test system for reliability analysis is used to compare the behavior of the proposed schemes when selecting different thresholds and percentages of load to be disconnected. Results are reported into detail and considerations on possible advantages and drawbacks also related to the framework provided by the electricity market are presented.

The paper proposes an optimal management strategy for a system composed by a battery and a photovoltaic power plant. This integrated system is called to deliver the photovoltaic power and to simultaneously provide droop-based primary frequency regulation to the main grid. The battery state-of-energy is controlled by power offset signals, which are determined using photovoltaic energy generation forecasts and predictions of the energy required to operate frequency regulation. A two level control architecture is developed. A day-ahead planning algorithm schedules the energy profile which is traded at the day-ahead market and defines the primary control reserve that the integrated system is able to provide in the considered day. During the day operations, a second level algorithm corrects the dispatched plan using updated information, in order to guarantee a continuous and reliable service. Both control algorithms take into account the uncertainties of the photovoltaic generation and of the frequency dynamics using stochastic optimization.

The recent worldwide effort on the environmental issue has led to new regulations on greenhouse gases emissions (GHG), both for land and marine applications. Nowadays, the extensive electrification of transportation systems is a promising choice for this purpose. In this perspective, algorithms for the optimum sizing and management of energy storage systems (ESSs) integrated into already operating shipboard power sys- tems are proposed in this work. The main aim of this method is reducing the power generation system fuel oil consumption, GHG emissions and management costs. This is applied to two case studies (i.e. a ferry and a platform supply vessel), of which load power profiles are available from the on-board integrated automation system (IAS). The results yielded show remarkable savings close to 6% and 32% along the whole ship's life horizon for the ferry and the platform supply vessel, respectively. These results prove that an optimal sizing combined with an optimum management of ESSs may significantly reduce the operative costs of shipboard power systems.