A new and improved particle swarm optimization (PSO) technique with adaptive inertia weight (w) based on success rate is proposed to find the optimal allocation of distributed generation (DG) and distribution static compensator (DSTATCOM) considering security limits. For the optimal sizing and siting of the device, technical, economic, and social objectives are considered. Logical and innovative indexes namely voltage profile enhancement index, benefit cost ratio, and emission cost benefit index are formulated to judge the impact of the device on the system. The developed algorithm is executed for both full search space (all load bus locations) and reduced search space (only unhealthy zone/locations) to prove that the optimal allocation is obtained only at the unhealthy zone. For the IEEE 33 and IEEE 69 bus systems, the proposed algorithm is compared with other techniques such as differential evolution, real-coded genetic algorithm, and PSO based on randomized inertia weight, and PSO based on linearly decreasing inertia weight. The best performance in terms of computational efficiency and solution quality is achieved for the proposed method. Further, a comparative performance analysis is presented between DG and DSTATCOM based on the economic, technical, and social impacts.

The recent large-scale integration of renewable energy sources (RESs) has caused numerous operational issues, thereby leading to the transfers of the power distribution system from passive to active ones. Therefore, a comprehensive planning of active distribution system (ADS) is required to address these challenges and ensure a reliable power supply. This study presents a stochastic framework for ADS planning using system framework design, RESs, and distribution-STATCOM (DSTATCOM). Unlike the traditional single-level planning, a bi-level planning model is proposed, in which the integrated optimal decision is aimed at upper-level planning, whereas RESs and DSTATCOM are incorporated in lower-level planning. Moreover, new and logical indexes are developed to address techno-economic and environmental aspects. To effectively consider the intermittency of load and RESs, a novel approach is developed considering probabilistic models and K-means algorithm. Finally, to solve the planning model, discrete rooted tree optimization (DRTO) technique is proposed and its superiority is established.

In the smart grid environment, there is a worldwide consensus concerning the high penetration of renewable energy resources (RESs) to cater a multitude of technical, economic, and social concerns. In this perspective, a combination of RESs and battery storage system (BSS) in co-ordination with the incorporation of distributed static compensator (DSTATCOM) is a smart, efficient, and viable approach to alleviate the power quality concerns. In this study, a comprehensive strategic model is designed to optimally incorporate RESs, BSS, and DSTATCOM with an aim to maximize technical, economic, and ecological benefit of the system. Logical and innovative formulas, such as benefit cost ratio, network voltage profile improvement index, environmental benefit index, and reliability indices, i.e., expected energy not served are formulated to assess the overall benefits in the present planning framework. A fuzzy-based extended version of nondominant sorting genetic algorithm NSGA II (E_NSGA II) is utilized for the strategic incorporation of the devices taken into account the practical security limits. The proposed multi-objective approach is applied on 69-bus radial distribution system considering the time variant practical load models and the obtained results are compared with other multi-objective algorithms to validate the efficacy and efficiency of the proposed approach.

In this circumstance of global warming, energy market deregulation, and enormous load growth, distribution network entails a proficient strategy to maintain the reliability and efficiency of the power service. Incorporation of solar photovoltaic (PV) system and battery storage (BS) in coordination with distributed static compensator (DSTATCOM) is a competent and practical approach to alleviate the power quality and reliability concern. In this study, a comprehensive strategic model is presented to optimally deploy PV, BS, and DSTATCOM to maximise voltage profile improvement, reliability, economic, and ecological benefit of the network. An accurate and precise novel voltage profile improvement indicator namely network voltage profile improvement index is proposed. Benefit–cost ratio and environment benefit index are proposed to quantify economic and environmental benefits, respectively. Similarly, reliability indices such as expected energy not served are used to appraise the system reliability. A fuzzy based extended version of NSGA II is utilised for the optimal deployment of the devices considering security limits. The proposed method is tested on 33‐bus and 69‐bus distribution networks considering time variant practical load models and the obtained results validate the efficacy and efficiency of the proposed method when compared with other multi‐objective algorithms.

Recently, there is a global consent regarding high penetration of solar energy (SE) resources to mitigate technical and environmental concerns. SE is intermittent in nature and if its incorporation is not properly planned, a multitude of serious technical issues will arise in the network. In this perspective, considering energy industry's practical aspects, a comprehensive robust practical planning model is presented in this paper to optimally integrate intermittent SE source with battery and DSTATCOM to maximize reliability, financial, environmental, and technical benefit of the system. Voltage unbalance in system creates varieties of problems such as poor power quality, higher power loss, and lesser system efficiency. However, owing to lack of proper industrial real data, researchers were unable to judge and incorporate the voltage unbalance issues, which are significant for the planning engineers to provide efficient solutions. Keeping this into view, the proposed method is tested on the real three-phase unbalanced 240-node network of USA where the customers are equipped with smart meters to provide real power data for realistic analysis. K-means clustering algorithm is utilized to generate appropriate number of scenarios that can accurately include the intermittency of solar power generation and load. A multi-objective algorithm namely extended version of NSGA II (E_NSGA II) is utilized for the optimal allocation of the devices. Compare to other algorithms, results reveal the competence and robustness of the proposed planning methodology for optimal allocation of SE with battery storage and DSTATCOM in order to maximize the overall benefits of the three-phase network.