Energy system is undergoing remarkable changes due to many factors including increase use of renewables, environmental consideration and technological advancements. This demands for new tools and methods for its efficient and smooth operation. This work proposes distributed demand response application using Multi-Agent System (MAS) for improving voltage in distribution network at port city. Contract-Net-Protocol (CNP) based scheme was used for communication and coordination between agents. A co-simulation framework including power system simulator and agent environment was used to evaluate the proposed MAS based approach. A test network including variable and intermittent renewable generation sources (wind, pv), flexible loads (reefers), non-flexible loads was used to investigate the MAS based approach. Results show that MAS based approach is quite effective for demand response application.

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This paper presents novel separate methods for finding optimal locations, sizes of multiple distributed generators (DGs) simultaneously and operational power factor in order to minimize power loss and improve the voltage profile in the distribution system. A load concentration factor (LCF) is introduced to select the optimal location(s) for DG placement. Exact loss formula based analytical expressions are derived for calculating the optimal sizes of any number of DGs simultaneously. Since neither optimizing the location nor optimizing the size is done iteratively, like existing methods do, the simulation time is reduced considerably. The exhaustive method is used to find the operational power factor, and it is shown with the results that the losses are further reduced and voltage profile is improved by operating the DGs at operational power factor. Results for power loss reduction and voltage profile improvement in IEEE 37 and 119 node radial distribution systems are presented and compared with the the loss sensitivity factor (LSF) method, improved analytical (IA) and exhaustive load flow method (ELF). The comparison for operational power factor and other power factors is also presented.@en

A method of reducing active power losses in distribution networks by simultaneous placement of optimally sized DGs is presented in this paper. Optimal DG sizes are calculated using analytical expressions in Python environment. IEEE 37 node test system, developed in DigSILENT PowerFactory, is used for the simulations. Three different use cases are presented in this work for validation and comparison. The active power loss reduction is compared with the base case (without any DGs). Different DG types, based on active and reactive power output, are considered during this work. The analytical expressions for simultaneous sizing proved their usefulness for loss minimization in radial networks. The complete study is done for the cases of three and four DGs placement in the system. To highlight the expanded capabilities of proposed method, it is applied to both the static and time varying load conditions.@en

The ongoing transformation of the traditional power system into a more intelligent Smart Grid has many benefits. However, it also brings numerous challenges. The system is becoming more complex, making the existing domain specific tools less relevant. This paper presents a flexible co-simulation framework that supports multiple simulators, including multiagent platform, for a detailed evaluation of the impact and inter-dependencies between the cyber and physical components of the system. Two case studies have been evaluated with different Smart Grid applications, modeled and simulated with different simulators and coupled into the developed co-simulation framework. The results show the flexibility and useability of the proposed co-simulation framework.@en

Integration of distributed generation in power system challenges operation and management of power distribution network. This paper presents Modified Bat Algorithm for Optimal Reactive Power Dispatch (OPRF) of distributed generation for voltage support in distribution network. An objective function with constraints of voltage, DG reactive power, thermal limits of lines is presented. A modified IEEE 37 node test feeder with variable generation was used to validate the proposed algorithm. A co-simulation setup was used in which electric grid was developed in Power system tool (DigSILENT PowerFactory) and Modified Bat Algorithm was developed in Python. Results showed that Modified Bat Algorithm is quite effective for voltage profile improvement and loss minimization.@en