This paper presents an improved load flow technique for a modern distribution system. The proposed load flow technique is derived from the concept of the conventional backward/forward sweep technique. The proposed technique uses linear equations based on Kirchhoff’s laws without involving matrix multiplication. The method can accommodate changes in network structure reconfiguration by involving the parent–children relationship between nodes to avoid complex renumbering of branches and nodes. The IEEE 15 bus, IEEE 33 bus and IEEE 69 bus systems were used for testing the efficacy of the proposed technique. The meshed IEEE 15 bus system was used to demonstrate the efficacy of the proposed technique under network reconfiguration scenarios. The proposed method was compared with other load flow approaches, including CIM, BFS and DLF. The results revealed that the proposed method could provide similar power flow solutions with the added advantage that it can work well under network reconfiguration without performing node renumbering, not covered by others. The proposed technique was then applied in Tanzania electric secondary distribution network and performed well.

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Metaheuristic algorithms have become popular in solving engineering optimization problems due to their advantages of simple implementation and the ability to find near-optimal solutions for complex and large-scale problems. However, most applications of metaheuristic algorithms consider centralized design, assuming that all possible solutions are available in one machine or controller. In some applications, such as power systems, especially DG coordination, centralized design may not be efficient. This work integrates a multi-agent system (MAS) into a metaheuristic algorithm for enhanced performance. In a proposed multi-agent framework, the agent implements a metaheuristic algorithm and uses shared information with neighbours as input to optimize the solutions. In this study, a new distributed Symbiotic Organism Search (SOS) algorithm has been proposed and tested in the proposed multi-agent framework. The proposed algorithm is termed a multi-agent-based symbiotic organism search algorithm (MASOS). The MASOS has been tested and compared with other proficient algorithms through statistical analysis using benchmark functions. The results show that the proposed MASOS solves the considered benchmark functions efficiently. Then MASOS was tested for DGs coordination considering load variations in the Tanzanian electrical distribution network. The results show that the coordination of DG using the proposed algorithm reduces power loss and improves the voltage profiles of the power system.

The increased penetration of distributed energy resources (DERs) technologies to residential users has fostered the need for DERs integration and control methods in the secondary distribution networks (SDN). In order to reap the potential advantages of DERs and achieve their inclusion in the electrical power system while avoiding their negative impacts, the DERs should be optimally placed and sized. Considering the nature of electrical networks and DER operations, the DERs placement is a nondeterministic polynomial hard (NP-hard) optimization problem. Metaheuristic algorithms are efficient for solving DER placement problems. Metaheuristic algorithms for DER placement in SDN involve high computational effort, theoretical convergence assumptions that cannot be satisfied in the real world and dependence on parameter settings. Therefore, this study proposes a DER placement algorithm that employs a cloud-based model symbiotic organism search algorithm (CMSOS). The CMSOS is attributed to simple implementation and computation, good convergence, and parameter independence. The electrical network segment taken for Tanzania’s electrical distribution network was used for testing the algorithms, considering power loss and voltage deviations. Results show that using DERs in the proposed locations reduces power loss by 89.3%. The convergence profile shows that the proposed CMSOS-based algorithm converges faster than the conventional symbiotic organism search algorithm (SOS). Keywords:    Metaheuristic Algorithms, Symbiotic Organism Search, DER Placements, Radial Distribution Network, Cloud-based model