• Energy Research

The concept of electrical-mobility in opposition to the present oil-mobility is becoming even more attracting worldwide. Fast Charging Station (FCS) refers charging stations with nominal power equal or higher than 50 kW. Consequently, FCS requires high power and they must be connected to MV networks. For that reasons, it is crucial to analyze these situations and to model the impact of FCS on the electric network, in order to correctly plan the expansion of the MV system. In the paper, specific studies will be performed on a real MV distribution network of the A2A utility sited in the district of Brescia (Italy), in order to quantify the impact of the FCS and to identify the most suitable planning solutions needed to allow the effective integration of EV and boost electric mobility. (4 pages)

The European strategic long-term vision underlined the importance of a smarter and flexible system for achieving net-zero greenhouse gas emissions by 2050. Distributed energy resources (DERs) could provide the required flexibility products. Distribution system operators (DSOs) cooperating with TSO (transmission system operators) are committed to procuring these flexibility products through market-based procedures. Among all DERs, battery energy storage systems (BESS) are a promising technology since they can be potentially exploited for a broad range of purposes. However, since their cost is still high, their size and location should be optimized with a view of maximizing the revenues for their owners. Intending to provide an instrument for the assessment of flexibility products to be shared between DSO and TSO to ensure a safe and secure operation of the system, the paper proposes a planning methodology based on the non-dominated sorting genetic algorithm-II (NSGA-II). Contrasting objectives, as the maximization of the BESS owners’ revenue and the minimization of the DSO risk inherent in the use of the innovative solutions, can be considered by identifying trade-off solutions. The proposed model is validated by applying the methodology to a real Italian medium voltage (MV) distribution network.

The necessity for flexible electric systems, changing regulatory and economic scenarios, energy savings and environmental impact are providing impetus to the development of distributed generation, which is predicted to play an increasing role in the future electric power system; with so much new distributed generation (DG) being installed, it is critical that the power system impacts be assessed accurately so that DG can be applied in a manner that avoids causing degradation of power quality, reliability and control of the utility system. Considering that uncertainties on DG power production are very relevant and different scenarios have to be taken into consideration, traditional deterministic planning techniques should not be used to take the right decisions. In this paper a three step procedure, based on genetic algorithms and decision theory, is applied to establish the best distributed generation siting and sizing on an MV distribution network, considering technical constraints, like feeder capacity limits, feeder voltage profile and three-phase short circuit currents in the network nodes. In the last part of the paper the procedure is applied to an actual distribution network.

Historically, the distribution network has been planned in order to operate in radial configuration. Protective relays are adopted to detect system abnormalities and to execute appropriate commands to isolate swiftly only the faulty component from the healthy system. Nowadays, the regulation schemes implemented by Regulators require that Distribution Companies reduce number and duration of supply interruptions by adopting new strategies in order to identify and isolate faults along distribution feeders. Two optimization problems arise: finding the optimal location of circuit breakers and the coordination among the overcurrent relay characteristics. In the paper, a novel algorithm to solve the overcurrent relay allocation and coordination problem in distribution networks based on genetic algorithm is proposed. Examples derived by a representative distribution network are presented. (6 pages)

The paper describes some results of a preliminary analysis of a pilot project for the exploitation of energy storage devices on the distribution system. The storage has been used for load levelling and voltage support and its performances have been obtained with software simulations based on real data. Emphasis has been placed on the importance of the optimal sizing and siting of the storage device.

This paper presents a network reconfiguration method for power flow optimization and, consequently, loss reduction in distribution networks with Distributed Generation (DG). The network reconfiguration problem is formulated and solved using a simple linear programming approach. Optimal configurations are determined by considering the effects of DG outputs, load variations, and various other contingences such as faults and maintenance outages. Demand Side Management actions have also been taken into account. Simulation results for test distribution network confirmed the effectiveness of the proposed method.

Integration of Photovoltaics (PV) with Energy Storage Systems (ESS) and Demand Side Management (DSM) is an innovative way that could transform a building into a self-sufficient nanogrid. Such an approach could act as an energy rehabilitation solution and contribute to alleviating the technical problems posed to the electrical grid from high consumption of buildings and high PV injections. The scope of this paper is to investigate the potential for achieving high level of building self-sufficiency using hybrid PV-ESS-DSM solutions. Towards this direction this work analyzes the self-consumption and self-sufficiency rates for various installations equipped with PV and ESS with different climatic and load demand characteristics. Moreover, the potential for increasing these indices under DSM is examined aiming to maximize the utilization of renewable energy produced on-site.

The engineering aspects of system planning require various objectives to be simultaneously accomplished in order to achieve the optimality of the power system development and operation. These objectives usually contradict each other and cannot be handled by conventional single optimization techniques, while multi-objective (MO) methods fit naturally. This is the case of the placement of a large amount of distributed generation (DG) into an existing distribution network, that has simultaneously the potentiality of achieving great savings (e.g. deferment of investments and reduction of power losses) or causing technical problems (e.g. overvoltages and/or overloads), depending on the DG size and location. In this paper, an evolutionary MO procedure for the optimal DG siting and sizing is proposed. The goal of this methodology is to establish the best penetration level of DG, which maximizes the benefits of the presence of generators in a distribution network, as well to limit the network performance deterioration due to DG not connected in optimal locations. An application example is presented to demonstrate the effectiveness of the proposed procedure.

Active distribution networks have the capability to allow the distributed energy resources integration at reasonable costs, opening new business opportunities. Although the sharing of responsibility among the system stakeholders (e.g., the Civil Society, the DSO and DER owners) is essential, undeniably they pursue different, and sometimes opposite goals. The authors, by adopting multi objective programming to simulate the behavior of the stakeholders, aim at assessing how the system players will drive the distribution evolution under the influence of different regulatory environments. All the scenarios have been analyzed on a case study representative of a typical distribution system. The results presented can help Regulators to define a fairer asset and performance based distribution revenue.