Emilio Ghiani

A multiobjective evolutionary algorithm for the sizing and siting of distributed generation

In the restructured electricity industry, the engineering aspects of planning need to be reformulated even though the goal to attain remains substantially the same, requiring various objectives to be simultaneously accomplished to achieve the optimality of the power system development and operation. In many cases, these objectives contradict each other and cannot be handled by conventional single optimization techniques. In this paper, a multiobjective formulation for the siting and sizing of DG resources into existing distribution networks is proposed. The methodology adopted permits the planner to decide the best compromise between cost of network upgrading, cost of power losses, cost of energy not supplied, and cost of energy required by the served customers. The implemented technique is based on a genetic algorithm and an /spl epsiv/-constrained method that allows obtaining a set of noninferior solutions. Application examples are presented to demonstrate the effectiveness of the proposed procedure.

Evaluation of uncertainty in measurements based on digitized data

The paper deals with the uncertainty in measurement based on digital signal processing algorithms, like those achievable with the virtual instruments. The correct estimation of bias and uncertainty is discussed with reference to a simple case study. Three possible approaches to this question are examined and compared. It is shown how a Monte Carlo method, based on numerical simulations and implemented with commercial software packages, can allow virtual instruments to perform an auto-evaluation of both bias and uncertainty affecting their results. Some theoretical considerations, computer simulations and experimental tests are shown to support the proposed technique.

A multi-objective formulation for the optimal sizing and siting of embedded generation in distribution networks

The optimal power system planning is achieved when various objectives are simultaneously attained: in many cases, these objectives contradict each other and cannot be handled by conventional single optimisation techniques. The aim of this paper is to analyse sizing and siting problems, related to the presence of embedded generation (EG) in distribution networks, in order to achieve the best compromise between cost of network upgrading, cost of power losses, cost of energy not supplied, power quality cost (e.g. aging due to harmonic distortion), and the cost of energy required by the served customers. A multi-objective technique is used to minimise more than one objective simultaneously: the implemented genetic algorithm applies the /spl epsi/-constrained technique to obtain a compromised non-inferior solution. Numerical examples are presented to demonstrate the properties of the proposed algorithm.

Optimal reconfiguration of distribution networks according to the microgrid paradigm

In the last twenty years power systems observed important changes at the distribution level, due to the presence of distributed generation and the changing towards MV active networks, as well the institutional, regulatory and commercial reorganization. In this new scenario, among the various opportunities related to the use of DG, there is the opportunity of a partition of the distribution network in cells or microgrids. In order to find the optimal combination of microgrids, an algorithm able to deal with the reconfiguration of distribution systems is proposed. In the search of the best configuration among different combinations of microgrids, the algorithm uses a Sequential Monte Carlo simulation technique. The final structure found by the algorithm maximizes the sum of the savings in both the cost of energy purchasing and the cost of service interruptions

Voltage profile optimization with distributed generation

A novel approach for voltage and reactive power control in active distribution networks is proposed in this paper. The purpose of the methodology is to achieve the overall objective of minimum network costs and, at the same time, allows finding a proper dispatch schedule for the distributed generators connected to the network such that voltage profile is optimized. The voltage regulation procedure follows iteratively two steps combining a genetic algorithm with a linearly constrained optimization method. Firstly, the genetic algorithm set the most promising distributed generation siting and sizing configurations and, secondly, for each configuration, the constrained optimization method is applied in order to find the operating point for all distributed generators that optimizes the voltage profile. The procedure efficiency has been tested in a real distribution network. The results show that distributed generation may offer a valuable opportunity to enhance voltage profile in distribution networks and can significantly impact the planning stage.

Integration of renewable energy sources, energy storage systems, and electrical vehicles with smart power distribution networks

In order to increase efficiency in the distribution of electrical energy, optimize energy consumption and increase the percentage of energy from renewable sources, thereby reducing emissions of greenhouse gases, the distribution networks and the equipment connected to them should be made more intelligent. The development of the future energy system will be based on planning and management of the distribution system in accordance with the philosophy of Smart grid (SG). This approach involves the extensive use of Information and Communication Technology (ICT) and innovative control systems in order to enable the realization of smart distribution systems, the active participation of demand, the availability of energy storage, as well as the integration of renewable energy sources (RES) and Distributed Generation (DG), as well as the growing number of electric vehicles. In the paper a review of the main current challenges and possible solutions to open problems in the development of smart distribution grids are presented.

A Multi-Objective Approach to Maximize the Penetration of Distributed Generation in Distribution Networks

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

Optimal location of biogas and biomass generation plants

Biomass refers to renewable energy coming from biological material such as trees, plants, manure, and sometimes wastes. The effectiveness of biomass power production requires the use of optimization algorithm to take into account biomass availability, transportation and power facilities as well as all the territory related constraints. The integration of optimization tools within Geographic Information Systems allows better performances. In the paper a specialized genetic algorithm allows finding the optimal biomass power plants distribution in Sardinia (Italy) by maximizing the economic benefit of private investors. The algorithm may be used in a very general way and integrated in the most commercial GIS softwares.

Active distribution network reliability assessment with a pseudo sequential Monte Carlo method

The increasing penetration of renewable intermittent generation imposes challenges on the existing distribution infrastructure and the system operator. New power flow patterns may require changes to control strategies, enhanced distribution automation, enforcement of distribution network infrastructure and/or greater degrees of information management and control according to the active distribution network paradigm (ADN). ADN might improve the quality of service or might lead to a more risky distribution system depending on the reliability of its communication and management system. In order to provide a measure of this risk, the reliability of ADNs operation is assessed in the paper with a pseudo sequential Monte Carlo method. The procedure is used to assess the reliability of a given active distribution network in different real case scenarios.

Uncertainty estimation for DSP‐based power quality measurements

This paper deals with the uncertainty in digital measurement systems designed for power quality applications. The main goal of this work is to evaluate such uncertainty by means of a Monte Carlo method recently proposed in the literature. The accuracy of the measurement result obtained with a DSP‐based instrument for power quality metering depends on the behavior of the devices located in both the conditioning block and A/D conversion stage: it is thus necessary to consider the uncertainties introduced by each component of the system and the propagation of their effects through the measurement chain. Here, the uncertainty is estimated starting from the technical specifications provided by the manufacturers of these devices. Experimental results are reported to show the importance of some concerns about the practical implementation of the proposed methodology in a real instrument.