Reliability analysis of distribution systems with photovoltaic generation using a power flow simulator and a parallel Monte Carlo approach

This paper presents a Monte Carlo approach for reliability assessment of distribution systems with distributed generation using parallel computing. The calculations are carried out with a royalty-free power flow simulator, OpenDSS (Open Distribution System Simulator). The procedure has been implemented in an environment in which OpenDSS is driven from MATLAB. The test system is an overhead distribution system represented by means of a three-phase model that includes protective devices. The paper details the implemented procedure, which can be applied to systems with or without distributed generation, includes an illustrative case study and summarizes the results derived from the analysis of the test system during one year. The goal is to evaluate the test system performance considering different scenarios with different level of system automation and reconfiguration, and assess the impact that distributed photovoltaic generation can have on that performance. Several reliability indices, including those related to the impact of distributed generation, are obtained for every scenario.Postprint (published version

Testing in the distributed test architecture: An extended abstract

Some systems interact with their environment at a number of physically distributed interfaces/ports and when testing such a system it is normal to place a local tester at each port. If the local testers cannot interact with one another and there is no global clock then we are testing in the distributed test architecture and this can introduce additional controllability and observability problems. While there has been interest in test generation algorithms that overcome controllability and observability problems, such algorithms lack generality since controllability and observability problems cannot always be overcome. In addition, traditionally only deterministic systems and models have been considered despite distributed systems often being non-deterministic. This paper describes recent work that characterized the power of testing in the distributed test architecture in the context of testing from a deterministic finite state machine and also work that investigated testing from a non-deterministic finite state machine and testing from an input output transition system. This work has the potential to lead to more general test generation algorithms for the distributed test architecture

Methodology for testing loss of mains detection algorithms for microgrids and distributed generation using real-time power hardware–in-the-loop based technique

The effective integration of distributed energy resources in distribution networks demands powerful simulation and test methods in order to determine both system and component behaviour, and understand their interaction. Unexpected disconnection of a significant volume of distributed generation (DG) could have potentially serious consequences for the entire system [1], this means DG sources can no longer be treated as purely negative load. This paper proposes a method of testing loss-of-mains (LOM) detection and protection schemes for distributed energy resources (DER) using real-time power hardware-in-the-loop (RT PHIL). The approach involves connecting the generator and interface under test (e.g. motor-generator set or inverter, controlled by an RTS – Real Time Station[3]) to a real-time simulator (an RTDS – Real Time Digital Simulator[2]) which simulates the local loads and upstream power system. This arrangement allows observation of the interaction with other controls in the network beyond the local microgrid area. These LOM schemes are of increasing importance because with growing penetration levels of distributed generation the network operator has less visibility and control of the connected generation. Furthermore when the generation and load in a particular network area are closely matched (e.g. a grid-connected microgrid), it becomes increasingly difficult to detect a loss of grid supply at the generator. This work builds upon the existing LOM testing methodology proposed in [4]. By utilising RT PHIL and a laboratory microgrid, the testing environment has been brought to a new level of functionality where system integrity can be more rigorously and realistically evaluated

Load Frequency Control: A Deep Multi-Agent Reinforcement Learning Approach

The paradigm shift in energy generation towards microgrid-based architectures is changing the landscape of the energy control structure heavily in distribution systems. More specifically, distributed generation is deployed in the network demanding decentralised control mechanisms to ensure reliable power system operations. In this work, a Multi-Agent Reinforcement Learning approach is proposed to deliver an agentbased solution to implement load frequency control without the need of a centralised authority. Multi-Agent Deep Deterministic Policy Gradient is used to approximate the frequency control at the primary and the secondary levels. Each generation unit is represented as an agent that is modelled by a Recurrent Neural Network. Agents learn the optimal way of acting and interacting with the environment to maximise their long term performance and to balance generation and load, thus restoring frequency. In this paper we prove using three test systems, with two, four and eight generators, that our Multi-Agent Reinforcement Learning approach can efficiently be used to perform frequency control in a decentralised way

A multiarchitecture parallel-processing development environment

A description is given of the hardware and software of a multiprocessor test bed - the second generation Hypercluster system. The Hypercluster architecture consists of a standard hypercube distributed-memory topology, with multiprocessor shared-memory nodes. By using standard, off-the-shelf hardware, the system can be upgraded to use rapidly improving computer technology. The Hypercluster's multiarchitecture nature makes it suitable for researching parallel algorithms in computational field simulation applications (e.g., computational fluid dynamics). The dedicated test-bed environment of the Hypercluster and its custom-built software allows experiments with various parallel-processing concepts such as message passing algorithms, debugging tools, and computational 'steering'. Such research would be difficult, if not impossible, to achieve on shared, commercial systems

Voltage stability assessment for distrbuted generation in islanded microgrid system

The increasing energy demands are stressing the generation and transmission capabilities of the power system. Distributed generation (DG), which generally located in distribution systems, has the ability to meet some of the growing energy demands. However, unplanned application of individual distributed generators might cause other technical problems. The microgrid concept has the potential to solve major problems arising from large penetration of DG in distribution systems. A microgrid is not a forceful system when it is compared to a power system. This project proposes a simulation approach to study voltage stability index (VSI) and voltage stability analysis in microgrid system for the improvement of the dynamic voltage stability in a microgrid in case of the dynamic voltage insufficiency. A model of IEEE-14 Bus System has been presented as a case study of an islanded microgird system. This project also presented line voltage stability index analysis which accurately performs voltage stability analysis at each transmission line and precisely predicts voltage collapse on power systems. A formula to calculate VSI has been derived and applied on two cases on the system. To show the effectiveness of the proposed voltage stability analysis method, this approach is implemented in a microgrid test system (14-bus, 20 lines) in PSAT which is a MATLAB toolbox environment. The test system has four diesel DGs and a wind turbine connected with eleven constant loads. The dynamic simulation of the test system is carried out for various types of disturbances. Islanded mode of operation is considered in this study. Fast Voltage Stability Index (FVSI) and voltage stability analysis have been successfully implemented and analysed

The KALI multi-arm robot programming and control environment

The KALI distributed robot programming and control environment is described within the context of its use in the Jet Propulsion Laboratory (JPL) telerobot project. The purpose of KALI is to provide a flexible robot programming and control environment for coordinated multi-arm robots. Flexibility, both in hardware configuration and software, is desired so that it can be easily modified to test various concepts in robot programming and control, e.g., multi-arm control, force control, sensor integration, teleoperation, and shared control. In the programming environment, user programs written in the C programming language describe trajectories for multiple coordinated manipulators with the aid of KALI function libraries. A system of multiple coordinated manipulators is considered within the programming environment as one motion system. The user plans the trajectory of one controlled Cartesian frame associated with a motion system and describes the positions of the manipulators with respect to that frame. Smooth Cartesian trajectories are achieved through a blending of successive path segments. The manipulator and load dynamics are considered during trajectory generation so that given interface force limits are not exceeded

Optimal Power Dispatch of Distributed Generators in Direct Current Networks Using a Master–Slave Methodology that Combines the Salp Swarm Algorithm and the Successive Approximation Method

This paper addresses the Optimal Power Flow (OPF) problem in Direct Current (DC) networks by considering the integration of Distributed Generators (DGs). In order to model said problem, this study employs a mathematical formulation that has, as the objective function, the reduction in power losses associated with energy transport and that considers the set of constraints that compose DC networks in an environment of distributed generation. To solve this mathematical formulation, a master–slave methodology that combines the Salp Swarm Algorithm (SSA) and the Successive Approximations (SA) method was used here. The effectiveness, repeatability, and robustness of the proposed solution methodology was validated using two test systems (the 21- and 69-node systems), five other optimization methods reported in the specialized literature, and three different penetration levels of distributed generation: 20%, 40%, and 60% of the power provided by the slack node in the test systems in an environment with no DGs (base case). All simulations were executed 100 times for each solution methodology in the different test scenarios. The purpose of this was to evaluate the repeatability of the solutions provided by each technique by analyzing their minimum and average power losses and required processing times. The results show that the proposed solution methodology achieved the best trade-off between (minimum and average) power loss reduction and processing time for networks of any size