Optimization of Microgrid Battery Capacity using PSO with Considering Islanding Operation

Electrical is used for various activities in all sectors. Rapid increase of electricity demand recently, makes it necessary to have an even more efficient method for generating electricity. Renewable energy and the microgrid provides an integrated and alternative solution for electricity generation. In microgrid systems, energy storage devices are one of important aspects. Batteries are one kind of the energy storage technologies widely used in power system and hence, their suitable capacity must be determined in order to develop an effective system installation. In this research, sizing optimization of battery capacity is modeled as a minimization of microgrid battery capacity using the Particle Swarm Optimization/PSO algorithm with considering islanding operation of the system for effective battery installation. Results show that optimal battery capacity can be obtained and the developed computational model gives satisfactory results for the system under study.   Keywords: Battery, microgrid, energy storage system, PSO algorith

Reliability assessment of microgrid with renewable generation and prioritized loads

With the increase in awareness about the climate change, there has been a tremendous shift towards utilizing renewable energy sources (RES). In this regard, smart grid technologies have been presented to facilitate higher penetration of RES. Microgrids are the key components of the smart grids. Microgrids allow integration of various distributed energy resources (DER) such as the distributed generation (DGs) and energy storage systems (ESSs) into the distribution system and hence remove or delay the need for distribution expansion. One of the crucial requirements for utilities is to ensure that the system reliability is maintained with the inclusion of microgrid topology. Therefore, this paper evaluates the reliability of a microgrid containing prioritized loads and distributed RES through a hybrid analytical-simulation method. The stochasticity of RES introduces complexity to the reliability evaluation. The method takes into account the variability of RES through Monte- Carlo state sampling simulation. The results indicate the reliability enhancement of the overall system in the presence of the microgrid topology. In particular, the highest priority load has the largest improvement in the reliability indices. Furthermore, sensitivity analysis is performed to understand the effects of the failure of microgrid islanding in the case of a fault in the upstream network

A Framework of Integrating Manufacturing Plants in Smart Grid Operation: Manufacturing Flexible Load Identification

In the deregulated electricity markets run by Independent System Operator (ISO), a two-settlement (day-ahead and real-time) process is typically used to determine the electricity price to the end-use customers at different buses. In the day-ahead settlement, the demand is predicted at each bus based on the previous consumption behavior of the consumers and thus, Locational Marginal Price (LMP) can be determined and shared to the consumers. A significant gap is usually observed between the planned and real-time demands due to the uncertainties of the weather (temperature, wind-speed etc.), the intensity of business, and everyday activities. Therefore, a large price variation may occur in the real-time market and the dispatching plan needs to be adjusted to respond to the variation. To reduce the gap between the day-ahead and real-time dispatching plans, a modified framework, i.e., a three-settlement process considering the integration of the manufacturing plants into the existing two-settlement process is proposed in this study. The manufacturing end-use customers report the flexibility of their loads to the ISO so that the ISO can update the day-ahead price through an updated dispatching plan that utilizes the feedback of the load flexibility from the manufacturers. A mathematical model is developed to identify the flexible and non-flexible loads of the manufacturers. Particle Swarm Optimization (PSO) is used to solve this mathematical model and a case study is conducted to illustrate the effectiveness of the model

Power Loss Minimization in a Radial Distribution Network by Optimal Sizing and Placement of Energy Storage Units

It is possible to reduce distribution losses by strategically placing and sizing DG and BESS sources. Assuring low loss requires strategically placing the aforementioned devices; otherwise, the system may experience either under- or overvoltage. It is preferable to choose bus stations with less risk for loss. The proposed approach tries to pinpoint the optimal BESS size and placement to cut down on investment and operating expenses while still achieving the desired level of energy reduction. The development of optimisation algorithms for finding and scaling BESS units is the fundamental focus of this study. Two such strategies are being explored here: the Genetic Algorithm (GA) and the Ant Colony Optimization Algorithm (ACOA). The goal function, like the original issue, seeks to minimise system-wide power losses while adhering to specified levels of equality and inequality. This article explores the appropriate capacity and placement of the DGs in a 33-bus radial distribution grid to reduce power dissipations. Matlab code is used to perform a simulation, and the results are put to use gauging the method's sturdiness

A Model to Estimate the Lifetime of BESS for the Prosumer Community of Manufacturers with OGS

Onsite generation system (OGS) with renewable sources for modern manufacturing plant is considered as a critical alternative energy source for the manufacturers. Prosumer community can be formed by aggregating such manufacturers to achieve a mutual goal of sustainable and resilient power system. As the sustainability of the network depends on the reliable operations of each component in the network, it is required to monitor the performance and lifetime of the components existed in the network. One of the critical as well as costly components used to enhance the reliability and performance of the network is the battery energy storage system (BESS). The paper proposes a lifetime estimation model for the BESS using an integrated approach of cellular automata and system dynamic (SD) to prevent any sudden power outage and build a reliable energy management framework for the community. The major factors such as energy demand of the manufacturing plant, intermittent generation from the OGS, energy sharing capability of the prosumers etc. are considered to simulate the model and determine the amount of battery degradation. Based on the estimated lifetime of the battery, the manufacturers further can control the energy management plan (charging/discharging scheme) to prolong the battery lifetime and ensure a reliable operation for the community. A numerical case study is simulated to illustrate the effectiveness of the model