On the impact of topological properties of smart grids in power losses optimization problems

Abstract

Power losses reduction is one of the main targets for any electrical energy distribution company. In this paper, we face the problem of joint optimization of both network topology and distributed generator parameters in a real smart grid. We consider a portion of the Italian electric distribution network managed by the ACEA Distribuzione S.p.A. located in Rome, Italy. We perform both the power factor correction (PFC) for tuning the generators and the distributed feeder reconfiguration (DFR) to set the optimal state of the breakers. This joint optimization problem is faced considering a suitable objective function and by adopting genetic algorithms as global optimization strategy. We analyze admissible network configurations, showing that some of these violate constraints on current and voltage at branches and nodes. Such violations depend only on topological properties of the network configurations. We perform experiments by feeding the simulation environment with real data concerning samples of dissipated and generated active and reactive power values of the ACEA smart grid. Results show that removing the configurations violating the electrical constraints from the solution space leads to important improvements in terms of power losses reduction. Moreover, we provide also an electrical interpretation of the phenomenon using graph-based pattern analysis techniques

State-of-the-Art Impacts of Smart Grid in the Power Systems Operation and Expansion Planning

“…A sensitivity analysis is also done on the planning costs and it is confirmed that the planning cost is highly sensitive to the energy price (for example, purchase and sale prices) 3 . In Possemato et al 4 the results show a reduction of energy losses due to the impact of Smart Grid. The reduction may seem insignificant, but the tests performed simulated only one hour of a specific day per year.…”

“…Both long-term and short-term problems are mathematically formulated with mixed nonlinear programming and solved through PSO co-evolutionary (CPCE) culture. In Possemato et al 4 efforts are focused on optimizing the performance of active distribution networks through a reduction of losses. The main techniques used in the literature are the correction of the Power Factor (PF) and reconfiguration of the distribution grid.…”

“…The optimization problem involving changing the network topology is complex because the status of the circuit breakers are not differentiable. To minimize power losses in smart grids in Possemato et al 4 , a model that considers the two techniques has been used. Therefore, the optimization problem is to find the optimum parameters and the topology best suited to the network, considering the constraints imposed on the voltage and current, as well as the topological constraints of the network.…”

(Hyper)graph Kernels over Simplicial Complexes

“…For this reason, graphs are commonly used to model a plethora of real-world, possibly complex, systems [ 1 ]. Notable examples include biological systems and chemistry [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ], social and collaboration networks [ 14 ], computer vision and image processing [ 15 , 16 , 17 , 18 ], natural language processing [ 19 , 20 , 21 , 22 ], and energy distribution networks [ 23 ].…”

Change-Point Methods on a Sequence of Graphs

“…A graph representation for data is appropriate in several fields, including physics, chemistry, neuroscience, and sociology [1], where the phenomena under investigations can be observed as a sequence of measurements whose pairwise relationships are relevant too and thus included in the data representation [2]. In these application scenarios, the identification of a possible change in the system behavior, a situation associated with anomalies or events to be detected in the sequence, is of particular interest; examples of applications that can be cast in this framework are functional brain networks [3] and power grids [4]. Further relevant applications cover data acquired from cyber-physical systems and the Internet of Things [5].…”