Reconfiguration of radial distribution networks is the basis of supply restoration after faults and of load balancing and loss minimization. The ability to automatically reconfigure the network quickly and efficiently is a key feature of autonomous and self-healing networks, an important part of the future vision of Smart Grids. We address the reconfiguration problem for outage recovery, where the cost of the switching actions dominates the overall cost: when the network reverts to its normal configuration relatively quickly, the electricity loss and the load imbalance in a temporary suboptimal configuration are of minor importance. Finding optimal feeder configurations under most optimality criteria is a difficult optimization problem. All known complete optimal algorithms require an exponential time in the network size in the worst case, and cannot be guaranteed to scale up to arbitrarily large networks. Hence most works on reconfiguration use heuristic approaches that can deliver solutions but cannot guarantee optimality. These approaches include local search, such as tabu search, and evolutionary algorithms. We propose using optimal informed search algorithms in the A* family, introduce admissible heuristics for reconfiguration, and demonstrate empirically the efficiency of our approach. Combining A* with admissible cost lower bounds guarantees that reconfiguration plans are optimal in terms of switching action costs.
An optimal power flow (OPF) model is developed in this paper, which includes three highly volatile energy sources: wind energy, photovoltaic energy, and electric vehicle as a vehicle to grid energy source. An interpretable probabilistic approach is implemented to estimate the power generation from the mentioned energy sources using different probability distribution functions. A single static synchronous series compensator is optimally configured and positioned in the combined grid power system to achieve balanced operation. This significant uncertainty imposed OPF model is solved by blending (a) chaotic mapping technique, and (b) Nelder–mead simplex method with conventional moth swarm algorithm (MSA) and the modified version of MSA is called chaotic simplex MSA (CSMSA). Proposed CSMSA's performance is evaluated analytically using extensive simulation approaches that include six different power‐related simulation instances considering IEEE 30 and 118‐bus test grid systems. The strength of the proposed CSMSA is examined by comparing its performance to five outstanding metaheuristic techniques.
This article specifically aims to prove the superiority of the proposed moth swarm algorithm (MSA) in view of wind-thermal coordination. In the present article, a probabilistic optimal power flow (POPF) problem is formulated to reflect the probabilistic nature of wind. Modelling of doubly fed induction generator (DFIG) is included in the proposed POPF to represent the wind energy conversion system (WECS). To reduce DFIG imposed deviation of bus voltage ancillary reactive power support is considered. Moreover, three different optimization techniques, namely, MSA, biogeography-based optimization (BBO), and particle swarm optimization (PSO) are independently applied for the minimization of active power generation cost for wind-thermal coordination, considering different instances in case of IEEE 30-bus and IEEE 118-bus system. From the simulation results, it is confirmed and validated that the proposed MSA performs considerably better than BBO and PSO.
This manuscript investigates the performance of the backtracking search algorithm (BSA) to minimize various objectives for an economical and secure power system. A variety of single and multi - objectives are delineated and solved. This manuscript also includes the valve-point loading effect alongside the objectives considered. The simulation has been computed in the IEEE 30-bus, IEEE 57-bus and IEEE 118-bus test network. The simulation outcomes as obtained by the proposed BSA and various algorithms are compared. Convergence curves are plotted to testify the characteristics of the proposed BSA for proceeding towards the global minima.
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