Eleonora Riva Sanseverino

Voltage regulation and power losses minimization in automated distribution networks by an evolutionary multiobjective approach

In this paper, the problem of voltage regulation and power losses minimization for automated distribution systems is dealt with. The classical formulation of the problem of optimal control of shunt capacitor banks and Under Load Tap Changers located at HV/MV substations has been coupled with the optimal control of tie-switches and capacitor banks on the feeders of a large radially operated meshed distribution system with the aim of attaining minimum power losses and the flattening of the voltage profile. The considered formulation requires the optimization of two different objectives; therefore the use of adequate multiobjective heuristic optimization methods is needed. The heuristic strategy used for the optimization is based on fuzzy sets theory. After a brief description of the general problem of optimal control of voltage and power losses in automated distribution networks, the most recent papers on the topic are reported and commented. Then the problem formulation and the solution algorithm are described in detail. Finally, numerical results on a large distribution system demonstrate that the proposed formulation and approach are effective and feasible for finding an optimal generalized dispatching schedule.

Heuristic-Based Shiftable Loads Optimal Management in Smart Micro-Grids

In this paper, an optimal power dispatch problem on a 24-h basis for distribution systems with distributed energy resources (DER) also including directly controlled shiftable loads is presented. In the literature, the optimal energy management problems in smart grids (SGs) where such types of loads exist are formulated using integer or mixed integer variables. In this paper, a new formulation of shiftable loads is employed. Such formulation allows reduction in the number of optimization variables and the adoption of real valued optimization methods such as the one proposed in this paper. The method applied is a novel nature-inspired multiobjective optimization algorithm based on an original extension of a glowworm swarm particles optimization algorithm, with algorithmic enhancements to treat multiple objective formulations. The performance of the algorithm is compared to the NSGA-II on the considered power systems application.

A Generalized Framework for Optimal Sizing of Distributed Energy Resources in Micro-Grids Using an Indicator-Based Swarm Approach

In this paper, a generalized double-shell framework for the optimal design of systems managed optimally according to different criteria is developed. Optimal design is traditionally carried out by means of minimum capital and management cost formulations and does not typically consider optimized operation. In this paper, the optimized multiobjective management is explicitly considered into the design formulation. The quality of each design solution is indeed defined by the evaluation of operational costs and capital costs. Besides, the assessment of the operational costs term is deduced by means of the solution of a multiobjective optimization problem. Each design solution is evaluated using the outcomes of a multiobjective optimization run: a Pareto hyper-surface in the n-dimensional space of the operational objectives. In the literature, commonly the evaluation of each design solution is carried out based on an approximate evaluation of the operational costs, not considering the real multiobjective optimized management. In this paper, such assessment is carried out using a suitable convergence indicator typically used for multiobjective optimization algorithms. The application is devoted to the problem of optimal sizing of distributed energy resources in medium voltage or low voltage microgrids. For this problem, the identification of the multiple operational impacts comes along with the solution of the optimal unit commitment of distributed generators. After the introductory section, the problem formulation is presented and an interesting application of the considered approach to the design of distributed energy sources in a microgrid is shown.

Optimal Electrical Distribution Systems Reinforcement Planning Using Gas Micro Turbines by Dynamic Ant Colony Search Algorithm

Distribution systems management is becoming an increasingly complicated issue due to the introduction of new energy trading strategies and new technologies. In this paper, an optimal reinforcement strategy to provide reliable and economic service to customers in a given time frame is investigated. In the new deregulated energy market and considering the incentives coming from the political and economical fields, it is reasonable to consider distributed generation (DG) as a viable option for systems reinforcement. In the paper, the DG technology is considered as a possible solution for distribution systems capacity problems, along several years. Therefore, compound solutions comprising the installation of both feeders and substations reinforcement and DG integration at different times are considered in the formulation of a minimum cost distribution systems reinforcement strategy problem. An application on a medium size network, hypothesizing a scenario of reinforcement also using as DG gas micro-turbines, is carried out using a novel optimization technique allowing the identification of optimal paths in trees or graphs. The proposed technique is the Dynamic Ant Colony Search algorithm

Minimum losses reconfiguration of MV distribution networks through local control of tie-switches

This paper deals with the problem of optimal reconfiguration of radial distribution networks for minimum loss operation. The proposed control strategy of the open-closed status of the tie switches is distributed, since every MV/LV node is provided with local controllers having some measured entities as input. It also does not prevent the system from the future implementation of centralized control; instead, it may represent the first step towards a complete automation of the distribution system. The proposed strategy is organized in hierarchic levels, the highest of which may be in the future a central control. After introducing the general problem of network reconfiguration, a review of the state of art on the subject is reported, even though the solution methodologies are usually related to the centralized formulation of the reconfiguration problem. The proposed local control strategy is outlined, and a detailed description of its different parts is reported with special attention to all the measures for a better performance of the system. Results of a number of simulation runs are reported in order to test the behavior of the proposed local control system in different possible operating conditions.

Transition of a distribution system towards an active network. Part I: Preliminary design and scenario perspectives

In an European perspective, the focus of Smart Grids initiatives (SET Plan - Strategic Energy Technology Plan) is strictly linked with the main commitment to achieve the goals of the Climate and Energy Package 20-20-20, at the light of the three main pillars of the European energy policy: competitiveness, sustainability and security of supply. Smart grid technologies will enable load levelling of the electrical grid, allowing a power company to run cleaner power sources - such as hydroelectric, wind, or solar - while reducing the need to use carbon-emitting gas, coal, or oil plants to meet peak demand. In this framework the proposed paper refers about the technical economical feasibility study and the preliminary design of a demonstrator of a distribution electrical system for the transition towards active networks. The study has been carried out by University of Palermo and ENEA (Italy), on a portion of real MV/LV distribution system of the research center ENEA of Casaccia (Rome, Italy).1

Transition of a distribution system towards an active network. Part II: Economical analysis of selected scenario

This paper outlines the economical issues related to the transition of the energy generation for a real MV/LV distribution system from a ‘fuel based’ one to a distributed and smart ‘renewables based’ one. It is the prosecution of a companion paper, which addressed the technical issues connected to such transition. The study has been carried out by University of Palermo and ENEA (Italy), on a portion of real MV/LV distribution system of the research center ENEA of Casaccia (Rome, Italy). The analysis is carried out for a specific scenario chosen among those proposed in the companion paper.

Evolving non-dominated solutions in multiobjective service restoration for automated distribution networks

Abstract The problem here dealt with is that of Service Restoration (SR) in automated distribution networks. In such networks, configuration and compensation level as well as loads insertion status can be remotely controlled. The considered SR problem should be handled using Multiobjective Optimization, MO, techniques since its solution requires a compromise between different criteria. In the adopted formulation, these criteria are the supply of the highest number of loads and the minimum power losses. The Authors propose a new MO approach, the Non-dominated Sorting Fuzzy Evolution Strategy, NS_FES, which uses part of the Non-dominated Sorting Genetic Algorithm, NSGA, proposed by K. Deb. The ability of NSGA to divide a population of solutions in classes of dominance allows a fruitful application of another efficient MO strategy already proposed and tested by the Authors (FES, Fuzzy Evolution Strategy). In this way, diversity and high quality of solutions is possible. After a brief description of the SR problem and a review of the approaches recently proposed in literature, the NS_FES solution strategy is presented in detail. Finally, test results using the three approaches (NSGA, FES, NS_FES) are carried out and compared.

An optimization approach for efficient management of EV parking lots with batteries recharging facilities

In this paper an optimization approach to devise efficient management strategies for Electric Vehicles parking lots is proposed. A Monte Carlo approach is used to evaluate the load consumption profile for groups of Electric Vehicles showing different features. The Monte Carlo approach allows to combine the different social and economical features affecting the commercial penetration of Electric Vehicles with the technical aspects. The basic feature to be assessed is the initial State Of Charge, which in turn depends on the distance travelled by the vehicle since the last recharge and thus by the usage of the vehicle (private, professional). The model is then used to optimize some objective function such as the losses minimization or the cost of purchased energy minimization. Finally, a Simulated Annealing algorithm is used to identify the time intervals, along the day, in which the Electric Vehicles should be put in charge to minimize technical or economical objectives. The objective function is evaluated using a probabilistic model based on Monte Carlo simulations.

An efficient iterative method for load-flow solution in radial distribution networks

In this paper, an efficient method for radial distribution networks solution is proposed. The efficiency of the presented strategy makes it suitable for distribution automation applications. The method is based on an iterative algorithm with some special procedures to increase the convergence speed; the bus voltages are considered as state variables according to approaches that are common in literature. After the presentation of the general problem and of the state of the art on the subject, the proposed methodology is treated in detail. It uses a simple matrix representation for the network topology and branch current flows management. The method has been applied to some test systems already used in literature so as to put into evidence its properties mostly in terms of calculation times reduction. The obtained results confirm that it outperforms other solution methods for radial networks.