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.

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.

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 (DG), which is predicted to play an increasing role in the electric power system of the future. With so much new distributed generation 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. For these reasons, the paper proposes a new software procedure, based on a Genetic Algorithm, capable to establish the optimal distributed generation allocation on an existing MV distribution network, considering all the technical constraints, like feeder capacity limits, feeder voltage profile and three-phase short circuit current in the network nodes.\u

Distribution System Operators have the duty of managing the distribution system safely and efficiently. Distribution revenues are generally based on asset value but revenue systems based to some extent on network performance (e.g. on the level of losses or reliability) are becoming common in many countries. Thus the distribution planner has to maximise the profit of the investments for the system development and to improve the performance of the system as well. In many case the goals are conflicting and multiobjective planning criteria may be adopted to reduce network costs and investments and to maximise some specific performance (e.g. network reliability). Furthermore, with multiobjective programming the distribution planner has the possibility of choosing a trade-off solution in a set of acceptable solutions by applying criteria to minimise risks in uncertain scenarios. These features are particularly useful whether, according to the EU directive EU/2003/54, Embedded Generation (EG) and DSM are considered as possible planning alternatives to be compared with more traditional development actions in the search for the best solution to planning problems. In this context, a methodology is presented based on multiobjective programming and decision theory which helps the decision maker to find the best development plan for the system by using the EG as a development option and by explicitly managing risks and uncertainties. In particular, the proposed methodology solves the problem of maximising the network performance by optimising some power quality indicators, like voltage quality and harmonic distortion, and by minimising the network costs (e.g. energy losses or upgrade investments) with an optimised use of EG. For this reason, the problem of the optimal sizing and siting of EG has been reformulated as a constrained, multiobjective, and nondifferentiable optimisation problem. The main idea is to apply a double trade-off procedure: the first trade-off, by means of a multiobjective ɛ-constrained technique, allows a wide family of good EG siting and sizing solutions for all the scenarios considered (e.g. associated to a different set of wind speed at all the possible locations); the second one allows isolating the most robust solutions. In this way the planner is completely free to drive the optimisation in a certain direction without loosing objectivity and generality. Simulation results obtained by using the proposed procedure on a test system demonstrate that alternatives disregarded by the decision theory application can result the most interesting ones.

Automatic devices that locate and automatically sectionalize faulted branches in MV voltage power distributions systems restrict drastically the extent of disruption caused by long power interruptions when properly positioned. Some algorithms and automatic calculation procedures are proposed here for determining the optimum number and location of automatic sectionalizing switching di vices. Using these algorithms, the optimal solution can be found for both radial and meshed systems. The procedure is based on Bellmann's optimality principle which, combined with thinning techniques yields finding the optimal solution in a few ms for real sire problems