Gianni Celli

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.

Distributed generation siting and sizing under uncertainty

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.

Optimal Integration of Distributed Energy Storage Devices in Smart Grids

Energy storage is traditionally well established in the form of large scale pumped-hydro systems, but nowadays is finding increased attraction in medium and smaller scale systems. Such expansion is entirely complementary to the forecasted wider integration of intermittent renewable resources in future electrical distribution systems (Smart Grids). This paper is intended to offer a useful tool for analyzing potential advantages of distributed energy storages in Smart Grids with reference to both different possible conceivable regulatory schemes and services to be provided. The Smart Grid Operator is assumed to have the ownership and operation of the energy storage systems, and a new cost-based optimization strategy for their optimal placement, sizing and control is proposed. The need to quantify benefits of both the Smart Grid where the energy storage devices are included and the external interconnected grid is explored. Numerical applications to a Medium Voltage test Smart Grid show the advantages of using storage systems related to different options in terms of incentives and services to be provided.

Optimal integration of energy storage in distribution networks

Energy storage, traditionally well established in the form of large scale pumped-hydro systems, is finding increased attraction in medium and smaller scale systems. Such expansion is entirely complementary to the wider uptake of intermittent renewable resources and to distributed generation in general, which are likely to present a whole range of new business opportunities for storage systems and their suppliers. In the paper, by assuming that Distribution System Operator has got the ownership and operation of storage, a new software planning tool for distribution networks able to define the optimal placement, rating and control strategies of distributed storage systems that minimize the overall network cost is proposed. This tool will assist the System Operators in defining the better integration strategies of distributed storage systems in distribution networks and in assessing their potential as an option for a more efficient operation and development of future electricity distribution networks.

DMS Cyber-Physical Simulation for Assessing the Impact of State Estimation and Communication Media in Smart Grid Operation

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.

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.

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.

Multi-objective programming for optimal DG integration in active distribution systems

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.

Neural networks for power system condition monitoring and protection

Abstract To maintain a good voltage quality level to customers in power distribution system, it is essential to minimise transients, line voltage dips and spikes due to a variety of causes, including fault occurrence, power interruption and large load changes. In case of private generating systems it is essential that ultra-rapid switching devices be used which cut off the customer plant from the utility system so quickly that the presence of voltage dips is not perceived by the industrial plants sensitive loads. These devices require very fast acquisition and control systems which permit to diagnose and, possibly, predict abnormal events. In this paper, a control methodology based on a locally recurrent-globally feed-forward neural network and on a neural classifier is proposed. It will be shown that it is possible to predict with good accuracy the value of the control variables based on previously acquired samples and use these values to recognise the kind of abnormal event that is about to occur on the network.

A comparison of distribution network planning solutions: Traditional reinforcement versus integration of distributed energy storage

Despite the radical changes that the electricity distribution system is currently facing and will have to face in the next decade, utilities still adopt a traditional approach for the expansion planning of their networks, based essentially on the fit and forget concept (only network reinforcements to cope with the worst case scenario). Although innovative solutions have been proposed in the last decade at the electricity distribution level (distributed energy storage, active management of distributed generation, demand side integration, etc.) and are becoming technically and economically feasible, they are not yet considered as viable planning alternatives. The reasons of this reluctance are the lack of ad hoc planning tools and business cases. In the paper, a novel planning tool for Active Distribution Networks is applied to a real MV distribution network of the A2A utility sited in the district of Brescia in order to prove the benefit of energy storage devices.