This work proposes a new multi-objective algorithm inspired from the navigation of grass hopper swarms in nature. A mathematical model is first employed to model the interaction of individuals in the swam including attraction force, repulsion force, and comfort zone. A mechanism is then proposed to use the model in approximating the global optimum in a single-objective search space. Afterwards, an archive and target selection technique are integrated to the algorithm to estimate the Pareto optimal front for multi-objective problems. To benchmark the performance of the algorithm proposed, a set of diverse standard multi-objective test problems is utilized. The results are compared with the most well-regarded and recent algorithms in the literature of evolutionary multi-objective optimization using three performance indicators quantitatively and graphs qualitatively. The results show that the proposed algorithm is able to provide very competitive results in terms of accuracy of obtained Pareto optimal solutions and their distribution.

Grasshopper optimization algorithm for multi-objective optimization problems

This paper proposes a day-ahead market framework for congestion management in smart distribution networks. The presented scheme provides a platform for collaboration between distribution-level market operator (DMO) and data traffic operator (DTO) to alleviate congested feeders such that data transmission traffic between market participants is effectively managed in a smart grid. In addition, a decentralized mechanism is developed for collaboration of electric vehicle (EV) aggregators with common clients to take advantage of EVs not only as flexible loads but also as mobile distributed storage (MDS) for congestion management. Moreover, the proposed framework outlines an administrative action for distribution system operator (DSO) to support the market when the decentralized competitions among distributed generation (DG) aggregators and EV aggregators do not fully relieve a serious congestion. The proposed day-ahead congestion management scheme is validated on an unbalanced 136-bus distribution system massively integrated with wind turbine DGs (WTDGs), photovoltaic DGs (PVDGs), diesel-engine DGs (DEDGs), and EVs.

A Market Framework for Decentralized Congestion Management in Smart Distribution Grids Considering Collaboration Among Electric Vehicle Aggregators

Optimal network reconfiguration and renewable DG integration considering time sequence variation in load and DGs

In an ac/dc hybrid distribution grid (ac/dc HDG), different ac distribution grids are connected through a voltage source converter (VSC)-based dc grid to operate the entire system in a secure and economic manner. This paper presents a decentralized optimal operation approach for running ac/dc HDG which can either work individually or collaborate with each other. Using the analytical target cascading approach, a three-level hierarchy consisting of dc grids, VSC stations and ac grids, is formulated to decompose the operation problem of ac/dc HDG. The obtained subproblems for these grid levels, as well as the interactions between them, are modeled and solved with a nested solution process. The simulation results show the effectiveness of the proposed decentralized approach.

A Decentralized Optimal Operation of AC/DC Hybrid Distribution Grids

Thanks to the recent improvements in renewable energy technologies throughout the world, distributed energy sources are now playing an undeniable role in supplying the electricity in distribution networks. This paper studies the impacts of utilizing distributed generation units on the task of network reconfiguration in distribution systems. Considering the importance of reducing voltage drops and voltage sags in distribution systems, network reconfiguration is formulated as a multiobjective optimization problem in this study to minimize these two objective functions. A Pareto-based metaheuristic optimization algorithm is proposed to identify a Pareto frontier representing the alternative high-quality suboptimal configurations. The proposed optimization method is tested on a 69-bus distribution system to demonstrate the performance of the algorithm.

The Impacts of Distributed Energy Sources on Distribution Network Reconfiguration

With ever increasing deployment of automation and communication systems in smart grids, distribution network reconfiguration is becoming a viable solution for improving the operation of power grids. A novel hybrid optimization algorithm is proposed in this paper that determines Pareto frontiers, as the candidate solutions, for multiobjective distribution network reconfiguration problem. The proposed hybrid optimization algorithm combines the concept of fuzzy Pareto dominance with shuffled frog leaping algorithm (SFLA) to recognize optimal nondominated solutions identified by SFLA. The local search step of SFLA is also customized for power systems application so that it automatically creates and analyzes only the feasible and radial configurations in its optimization procedure, which significantly increases the convergence speed of the algorithm. Moreover, an adaptive reliability-based frog encoding is introduced that supervises the algorithm to concentrate on more reliable network topologies. The performance of the proposed method is demonstrated on a 136-bus electricity distribution network.

mohammad sadegh payam

Papers

Pareto Dominance-Based Multiobjective Optimization Method for Distribution Network Reconfiguration