Susanna Mocci

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.

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.

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

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

Multi-agent control system for increasing hosting capacity in active distribution networks with EV

Electrical mobility is based on the usage of electric vehicle (EV) as the main future technology to combat greenhouse gas emissions. Detailed knowledge about the future charging profiles of EVs appears to be missing in Literature. Slow chargers will be spread in the LV network or concentrated in some parking lots and connected to the LV or MV network. In the paper, an intelligent and decentralized Multi-Agent System (MAS) for handling EV charging control and management in the LV distribution networks is proposed. The MAS optimization algorithm proposed allows designing a valid and effective EVs charging system, able to analyse and meet the EV charging needs to contribute to the voltage control of the distribution network.

Aggregated electric vehicles load profiles with fast charging stations

The concept of electrical-mobility in opposition to the present oil-mobility is becoming even more attractive worldwide. Fast Charging Station (FCS) refers charging stations with nominal power equal or higher than 50 kW. The impacts of EV charging on electricity grids is becoming an increasingly important subject of study, but detailed knowledge about the future charging profiles of EVs appears to be missing in Literature. The 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 FCS consumption, in order to correctly plan the expansion of the MV system. In the paper a Monte Carlo simulation methodology is proposed to model the aspects that influence the request of fast charge for EVs. The EV charge profiles should be used in the planning and in the EV impact analysis of the future networks.

Multiobjective Programming for the Optimal Sizing and Siting of Power-Electronic Interfaced Dispersed Generators

Dispersed Generation (DG) is often connected to the network with power-electronic interfaces for a proper coupling with the distribution networks (e.g. photovoltaic panels, fuel cells, microturbines, wind turbines, etc.). Such power electronics can perforin functions other than the supply of real power thus representing an opportunity to increase the value of DG as provider of new additional services. In particular, the innovation and the improvements in electronic devices allow using DG for the provision of ancillary services (voltage control, power quality disturbance compensation, and so on), representing, in this way, an interesting chance to improve the quality of the service. For these reasons, specific planning tools for the optimal siting and sizing of DG should be adopted to take into consideration the multiple and contrasting goals that the Distribution System Operators (DSO) strive to achieve. In the paper the optimal siting and sizing of DG connected through electronic interfaces is performed by means of multiobjective programming and an -constrained technique is used for the solution of the problem. Particular attention is paid to the modelling of electronic interfaces in order to obtain the required ancillary service provision. Simulation results on a test system demonstrate the usefulness of the proposed methodology as well as the advantages obtained by the use of electronic interfaces in comparison to solutions employing electric generators directly coupled to the distribution network.

Impact of measurement accuracy in a multi-agent control system for active distribution networks

The control of active demand, electric vehicles, distributed generation and storage devices is the key to offer services that increase security and quality of supply of power systems, improve energy efficiency and reduce the cost for energy. Decentralised control systems have the merit to allow the operation of many small customers with a reduced information flow by exploiting local information gathered from the field with intelligent meters. Smart metering and ICT are the enabling technologies to offer market opportunities to the consumers, and to provide the Distribution System Operator (DSO) information regarding the behaviour of the prosumers. In this paper, an accurate Multi-Agent System (MAS) for the direct control of active demand in the distribution networks has been carried out. In particular, different levels of knowledge regarding the load behaviour have been considered. The impact of such knowledge in the optimal pattern of load demand that minimizes costs and improves the Power Quality of the distribution system has been evaluated. The discussed results prove the utility of the proposed analysis for the optimal design of MAS control.