Optimal sizing and siting of smart microgrid components under high renewables penetration considering demand response

The purpose of this article is to determine the size and place of different components in microgrids (MGs) including renewable energy resources (RERs). Various factors like reliability, the uncertainty of wind speed, solar irradiance, load, and load growth are considered. The Ekbatan residential complex is studied as the pilot case study placed in Tehran, Iran. Ekbatan complex has three separate sets of buildings called phase 1, 2, and 3 considered as smart MGs. The multi‐objective optimisation problem is solved considering RERs uncertainties, improving reliability and power quality and minimizing power loss by particle swarm optimisation algorithm. Different constraints in terms of voltage, frequency, resources, and energy storage systems (ESSs) capacity are taken into consideration. The effect of load growth, photovoltaic (PV) and ESSs placement, changing the capital cost of RERs, and demand response of controllable loads are studied on optimal sizing and siting. The proposed method is tested on a wind turbine/PV/fuel cell (FC)/hydrogen tank MGs system and the optimal sizing and siting of mentioned sources could decelerate the rate of increase in the total cost of MG considering the load growth.©2019 IET. This paper is a postprint of a paper submitted to and accepted for publication in IET Renewable Power Generation and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Library.fi=vertaisarvioitu|en=peerReviewed

Optimal placement of distributed energy storage systems in distribution networks using artificial bee colony algorithm

The deployment of utility-scale energy storage systems (ESSs) can be a significant avenue for improving the performance of distribution networks. An optimally placed ESS can reduce power losses and line loading, mitigate peak network demand, improve voltage profile, and in some cases contribute to the network fault level diagnosis. This paper proposes a strategy for optimal placement of distributed ESSs in distribution networks to minimize voltage deviation, line loading, and power losses. The optimal placement of distributed ESSs is investigated in a medium voltage IEEE-33 bus distribution system, which is influenced by a high penetration of renewable (solar and wind) distributed generation, for two scenarios: (1) with a uniform ESS size and (2) with non-uniform ESS sizes. System models for the proposed implementations are developed, analyzed, and tested using DIgSILENT PowerFactory. The artificial bee colony optimization approach is employed to optimize the objective function parameters through a Python script automating simulation events in PowerFactory. The optimization results, obtained from the artificial bee colony approach, are also compared with the use of a particle swarm optimization algorithm. The simulation results suggest that the proposed ESS placement approach can successfully achieve the objectives of voltage profile improvement, line loading minimization, and power loss reduction, and thereby significantly improve distribution network performance

Optimal Sizing and Placement of Solar Cell Distributed Generator Suitable for Integrated Power System Environment

A novel fuzzified Clustered Gravitational Search Algorithm (CGSA) has been employed for solving multi-objective problem formulated for solar based distributed generation. Optimal sizing and placement of solar distributed generation is considered. High solar penetration can lead to high-risk level in power system reliability. In order to maintain the system reliability, solar power dispatch is usually restricted based on the reliability level of the system. Two conflicting objective functions such as power loss and reliability level of the system are also considered for solving optimal placement of solar distributed generation (SDG). Binary coded CGSA is employed for solving optimal placement of SDG and sizing is determined using real coded CGSA. The fuzzy membership function for each objective is designed and multi-objective optimal placement problem has been presented. The proposed method is validated on IEEE standard 69-bus radial distribution networks. The efficiency of the proposed optimization technique is validated by comparing the results with other results available in the existing articles

Stochastic Modeling and Planning of Wind-Based Distributed Generators in Distribution System

The increasing strain on the Earth resulting from pollution, climate change, and finite resources has established the development of renewable energy sourcing methods, such as wind, solar and geothermal energy. By reorganizing the power system structures, and the growth in customer demand, the development of Distributed Generation (DG) play a vital role in the power system planning. Furthermore, because of the inexhaustibility and cleanliness of the renewable DG units, they are inevitably the key to a sustainable energy supply infrastructure. Nevertheless, the random nature associated with the renewable DG units produces specific challenges that have to be addressed to accelerate the expansion of the renewable DG units in the distribution system. Firstly, a new method for the determination of the wind speed distribution based on hourly wind speed data is proposed. Thus, instead of using only the well-known unimodal distributions such as Weibull and Rayleigh, a combination of probability density functions (PDFs) is taken into account, considering four sets of parameters in which each set represents a distribution. Furthermore, this model enhances the likelihood of the estimated wind speed probabilities. The maximum likelihood estimation (MLE) method for finite mixture models through the expectation-maximization (EM) algorithm is used to estimate the optimal parameters of the mixture distribution. Then two types of error measurements assessed the performance of each unimodal and multimodal distribution. As a result, the mixture of Gamma (MoG) distribution returned the most accurate results. Secondly, the results of wind speed modeling will be used in the siting and sizing wind-based DG units. The methodology addresses a probabilistic generation load model that combines all possible operating conditions of the wind-based DG units and load levels with their probabilities. The objective of siting and sizing formulation is to minimize the annual energy losses of the system as well as keeping the system constraints such as voltage limits at different buses (slack and load buses) of the system, feeder capacity, discrete size of the DG units, maximum investment on each bus, and maximum penetration limit of DG units in an acceptable limit

Hosting Capacity Assessment of Distribution Systems

The increasing penetration of distributed energy resources (DERs) in distribution systems may result in a number of technical problems such as over-voltage, overloading, maloperation of protection systems and power quality issues. One approach to address the above-mentioned issues is upgrading the distribution network, which is quite costly. The second approach is to limit the penetration of DERs to the hosting capacity (HC), which is defined as the maximum DER capacity that can be installed in a system without violating the operational constraints. Understanding this concept can assist utilities to ensure the reliable operation of the system. There have been different studies to identify the HC in a system. Nevertheless, the uncertainties associated with the DERs and loads have not been addressed properly in such studies. Besides, it is very difficult to quantify the findings of those studies and make general conclusions, as they were often based on specific networks, while their methods is time consuming in a big distribution network. Furthermore, the impact of voltage control schemes and emerging technologies, such as electric vehicles (EVs) and battery energy storage systems (BESSs) on the HC have not been studied, adequately. Thus, in this thesis, we propose a suitable HC assessment framework, as well as utilize some of the conventional and emerging resources to increase the HC

Microgrids & District Energy: Pathways To Sustainable Urban Development

A microgrid is an energy system specifically designed to meet some of the energy needs of a group of buildings, a campus, or an entire community. It can include local facilities that generate electricity, heating, and/or cooling; store energy; distribute the energy generated; and manage energy consumption intelligently and in real time. Microgrids enable economies of scale that facilitate local production of energy in ways that can advance cost reduction, sustainability, economic development, and resilience goals. As they often involve multiple stakeholders, and may encompass numerous distinct property boundaries, municipal involvement is often a key factor for successful implementation. This report provides an introduction to microgrid concepts, identifies the benefits and most common road blocks to implementation, and discusses proactive steps municipalities can take to advance economically viable and environmentally superior microgrids. It also offers advocacy suggestions for municipal leaders and officials to pursue at the state and regional level. The contents are targeted to municipal government staff but anyone looking for introductory material on microgrids should find it useful

Modeling and Controlling a Hybrid Multi-Agent based Microgrid in Presence of Different Physical and Cyber Components

This dissertation starts with modeling of two different and important parts of the distribution power systems, i.e. distribution line and photovoltaic (PV) systems. Firstly, it studies different approximation methods and develops a new approach for simplification of Carson\u27s equations to model distribution lines for unbalanced power flow and short circuit analysis. The results of applying the proposed method on a three-phase unbalanced distribution system are compared with different existing methods as well as actual impedance values obtained from numerical integration method. Then steady state modeling and optimal placing of multiple PV system are investigated in order to reduce the total loss in the system. The results show the effectiveness of the proposed method in minimizing the total loss in a distribution power system.;The dissertation starts the discussion about microgrid modeling and control by implementing a novel frequency control approach in a microgrid. This study has been carried out step by step by modeling different part of the power system and proposing different algorithms. Firstly, the application of Renewable Energy Sources (RES) accompanied with Energy Storage Systems (ESS) in a hybrid system is studied in the presence of Distributed Generation (DG) resources in Load Frequency Control (LFC) problem of microgrid power system with significant penetration of wind speed disturbances. The next step is to investigate the effect of PHEVs in modelling and controlling the microgid. Therefore, system with different penetrations of PHEVs and different stochastic behaviors of PHEVs is modeled. Different kinds of control approaches, including PI control as conventional method and proposed optimal LQR and dynamic programming methods, have been utilized and the results have been compared with each other. Then, Multi Agent System (MAS) is utilized as a control solution which contributes the cyber aspects of microgrid system. The modeled microgrid along with dynamic models of different components is implemented in a centralized multi-agent based structure. The robustness of the proposed controller has been tested against different frequency changes including cyber attack implications with different timing and severity. New attack detection through learning method is also proposed and tested. The results show improvement in frequency response of the microgrid system using the proposed control method and defense strategy against cyber attacks.;Finally, a new multi-agent based control method along with an advanced secondary voltage and frequency control using Particle Swarm Optimization (PSO) and Adaptive Dynamic Programming (ADP) is proposed and tested in the modeled microgrid considering nonlinear heterogeneous dynamic models of DGs. The results are shown and compared with conventional control approaches and different multi-agent structures. It is observed that the results are improved by using the new multi-agent structure and secondary control method.;In summary, contributions of this dissertation center in three main topics. Firstly, new accurate methods for modeling the distribution line impedance and PV system is developed. Then advanced control and defense strategy method for frequency regulation against cyber intrusions and load changes in a microgrid is proposed. Finally, a new hierarchical multi-agent based control algorithm is designed for secondary voltage and frequency control of the microgrid. (Abstract shortened by ProQuest.)

Smart management strategies of utility-scale energy storage systems in power networks

Power systems are presently experiencing a period of rapid change driven by various interrelated issues, e.g., integration of renewables, demand management, power congestion, power quality requirements, and frequency regulation. Although the deployment of Energy Storage Systems (ESSs) has been shown to provide effective solutions to many of these issues, misplacement or non-optimal sizing of these systems can adversely affect network performance. This present research has revealed some novel working strategies for optimal allocation and sizing of utility-scale ESSs to address some important issues of power networks at both distribution and transmission levels. The optimization strategies employed for ESS placement and sizing successfully improved the following aspects of power systems: performance and power quality of the distribution networks investigated, the frequency response of the transmission networks studied, and facilitation of the integration of renewable generation (wind and solar). This present research provides effective solutions to some real power industry problems including minimizationof voltage deviation, power losses, peak demand, flickering, and frequency deviation as well as rate of change of frequency (ROCOF). Detailed simulation results suggest that ESS allocation using both uniform and non-uniform ESS sizing approaches is useful for improving distribution network performance as well as power quality. Regarding performance parameters, voltage profile improvement, real and reactive power losses, and line loading are considered, while voltage deviation and flickers are taken into account as power quality parameters. Further, the study shows that the PQ injection-based ESS placement strategy performs better than the P injection-based approach (in relation to performance improvement), providing more reactive power compensations. The simulation results also demonstrate that obtaining the power size of a battery ESS (MVA) is a sensible approach for frequency support. Hence, an appropriate sizing of grid-scale ESSs including tuning of parameters Kp and Tip (active part of the PQ controller) assist in improving the frequency response by providing necessary active power. Overall, the proposed ESS allocation and sizing approaches can underpin a transition plan from the current power grid to a future one

International Journal of Smart Grid and Clean Energy Research on battery storage system configuration in active distribution networks

Abstract In this paper, an optimizing model of battery energy storage system (BESS) capacity configuration for active distribution networks (ADNs) is proposed to mitigate the negative effect of distribution systems with distributed generations (DGs), in stability, economy and reliability perspectives. In details, this model involved the charging and discharging limitation and the operation constrains of BESS, as well as the power flow balance of distribution system. Moreover, three optimizing objectives have been presented for the optimal power profile of BESS, including minimizing the voltage fluctuation, reducing the feeder loss and maximizing the consecutive supplied power of ADN, respectively. After the optimization, the BESS capacity is calculated by estimation of the maximum charging and discharging energy. Finally, the simulation results are shown to illustrate the procedure of capacity configuration

Optimal Allocation Of Distributed Renewable Energy Sources In Power Distribution Networks

In this dissertation study, various methods for optimum allocation of renewable distributed generators (DGs) in both balanced and unbalanced distribution networks have been proposed, developed, and tested. These methods were developed with an objective of maximizing several advantages of DG integration into the current distribution system infrastructure. The first method addressed the optimal sitting and sizing of DGs for minimum distribution power losses and maximum voltage profile improvement of distribution feeders. The proposed method was validated by comparing the results of a balanced distribution system with those reported in the literature. This method was then implemented in a co-simulation environment with Electric Power Research Institute\u27s (EPRI) OpenDSS program to solve a three phase optimal power flow (TOPF) problem for optimal location and sizing of multiple DGs in an unbalanced IEEE-123 node distribution network. The results from this work showed that the better loss reduction can be achieved in less computational time compared to the repeated load flow method. The second and third methods were developed with the goal of maximizing the reliability of distribution networks by optimally sitting and sizing DGs and reclosers in a distribution network. The second method focused on optimal allocation of DGs and reclosers with an objective of improving reliability indices while the third method demonstrated the cost based reliability evaluation. These methods were first verified by comparing the results obtained in a balanced network with those reported in literature and then implemented on a multi-phase unbalanced network. Results indicated that optimizing reclosers and DGs based on the reliability indices increases the total cost incurred by utilities. Likewise, when reclosers and DG were allocated to reduce the total cost, the reliability of the distribution system decreased. The fourth method was developed to reduce the total cost incurred by utilities while integrating DGs in a distribution network. Various significant issues like capital cost, operation and maintenance cost, customer service interruption cost, cost of the power purchased from fossil fuel based power plants, savings due to the reduction in distribution power losses, and savings on pollutant emissions were included in this method. Results indicated that integrating DGs to meet the projected growth in demand provides the maximum return on the investment. Additionally, during this project work an equivalent circuit model of a 1.2 kW PEM fuel cell was also developed and verified using electro impedance spectroscopy. The proposed model behaved similar to the actual fuel cell performance under similar loading conditions. Furthermore, an electrical interface between the geothermal power plant and an electric gird was also developed and simulated. The developed model successfully eliminated major issues that might cause instability in the power grid. Furthermore, a case study on the evaluation of geothermal potential has been presented