Power Electronics Applications in Renewable Energy Systems

The renewable generation system is currently experiencing rapid growth in various power grids. The stability and dynamic response issues of power grids are receiving attention due to the increase in power electronics-based renewable energy. The main focus of this Special Issue is to provide solutions for power system planning and operation. Power electronics-based devices can offer new ancillary services to several industrial sectors. In order to fully include the capability of power conversion systems in the network integration of renewable generators, several studies should be carried out, including detailed studies of switching circuits, and comprehensive operating strategies for numerous devices, consisting of large-scale renewable generation clusters

Microgrid Energy Management

In IEEE Standards, a Microgrid is defined as a group of interconnected loads and distributed energy resources with clearly defined electrical boundaries, which acts as a single controllable entity with respect to the grid and can connect and disconnect from the grid to enable it to operate in both grid-connected or island modes. This Special Issue focuses on innovative strategies for the management of the Microgrids and, in response to the call for papers, six high-quality papers were accepted for publication. Consistent with the instructions in the call for papers and with the feedback received from the reviewers, four papers dealt with different types of supervisory energy management systems of Microgrids (i.e., adaptive neuro-fuzzy wavelet-based controls, cost-efficient power-sharing techniques, and two-level hierarchical energy management systems); the proposed energy management systems are of quite general purpose and aim to reduce energy usages and monetary costs. In the last two papers, the authors concentrate their research efforts on the management of specific cases, i.e., Microgrids with electric vehicle charging stations and for all-electric ships

Machine Learning and Data Mining Applications in Power Systems

This Special Issue was intended as a forum to advance research and apply machine-learning and data-mining methods to facilitate the development of modern electric power systems, grids and devices, and smart grids and protection devices, as well as to develop tools for more accurate and efficient power system analysis. Conventional signal processing is no longer adequate to extract all the relevant information from distorted signals through filtering, estimation, and detection to facilitate decision-making and control actions. Machine learning algorithms, optimization techniques and efficient numerical algorithms, distributed signal processing, machine learning, data-mining statistical signal detection, and estimation may help to solve contemporary challenges in modern power systems. The increased use of digital information and control technology can improve the grid’s reliability, security, and efficiency; the dynamic optimization of grid operations; demand response; the incorporation of demand-side resources and integration of energy-efficient resources; distribution automation; and the integration of smart appliances and consumer devices. Signal processing offers the tools needed to convert measurement data to information, and to transform information into actionable intelligence. This Special Issue includes fifteen articles, authored by international research teams from several countries

Smart Energy Management for Smart Grids

This book is a contribution from the authors, to share solutions for a better and sustainable power grid. Renewable energy, smart grid security and smart energy management are the main topics discussed in this book

Coordinated active power reduction strategy for voltage rise mitigation in LV distribution network

Integration of renewable energy systems by the utility, customers, and the third party into the electric power system, most especially in the MV and LV distribution networks grew over the last decade due to the liberalization of the electricity market, rising energy demand, and increasing environmental concern. The distributed rooftop PV system contributes to relieve the overall load, reduce losses, avoid conventional generation upgrade, and better matching of demand on the LV distribution network. Originally, the LV distribution network is designed for unidirectional current flow, that is from the substation to customers. However, a high penetration of rooftop solar PVs (with power levels typically ranging from 1 – 10 kW) may lead to the current flowing in the reverse direction and this could result in a sudden voltage rise. These negative impacts on the network have discouraged the distribution network operators (DNOs) to allow increased PV penetration in the LV distribution network because some customers load, and equipment are sensitive to voltage perturbation. Presently, the most applied voltage rise mitigation strategy for high rooftop solar PV penetration is the total disconnect from the LV distribution network when the voltage at the point of common coupling (PCC) goes above statutory voltage limits. However, the sudden disconnection of the PV system from the grid can cause network perturbation and affect the security of the network. This action may also cause voltage instability in the network and can reduce the lifetime of grid equipment such as voltage regulators, air conditioner etc. Due to this negative impact, different voltage rise mitigation strategies such as the active transformer with on load tap changers (OLTC), distributed battery energy storage system and reactive power support (D-STATCOM, etc.) have been used to curtail voltage rise in the distribution network. However, the implementation of D-STATCOM device on a radial LV distribution network results in high line current and losses. This may be detrimental to the distribution network. Therefore, in this thesis, a coordinated active power reduction (CAPR) strategy is proposed using a modified PWM PI current control strategy to ramp down the output power and voltage of a grid-tied voltage source inverter (VSI). In the proposed strategy, a reactive reference is generated based on the measured voltage level at the PCC using a threshold voltage algorithm to regulate the amplitude of the modulating signal to increase the off time of the high frequency signal which shut down the PV array momentary in an extremely short time and allow the VSI to absorb some reactive power through the freewheeling diode and reduce voltage. The proposed CAPR strategy was designed and simulated on a scaled down simple radial LV distribution network in MATLAB®/Simulink® software environment. The results show that the CAPR can ramp down the PV output power, reduce reverse power flow and reduce the sudden voltage rise at the point of common coupling (PCC) within ±5% of the standard voltage limit. The study also compares the performance of the proposed CAPR strategy to that of the distributed static compensator (D-STATCOM) and battery energy storage system (BESS) with respect to response time to curtail sudden voltage rise, losses and reverse power flow. The investigation shows that the D-STATCOM has the faster response time to curtail voltage rise. However, the voltage rise reduction is accompanied by high current, losses and reverse active power flow. The introduction of the BESS demonstrates better performance than the D- STATCOM device in terms of reverse power flow and losses. The CAPR strategy performs better than both D-STATCOM and BESS in terms of line losses and reverse power flow reduction

Optimal energy management for a grid-tied solar PV-battery microgrid: A reinforcement learning approach

There has been a shift towards energy sustainability in recent years, and this shift should continue. The steady growth of energy demand because of population growth, as well as heightened worries about the number of anthropogenic gases released into the atmosphere and deployment of advanced grid technologies, has spurred the penetration of renewable energy resources (RERs) at different locations and scales in the power grid. As a result, the energy system is moving away from the centralized paradigm of large, controllable power plants and toward a decentralized network based on renewables. Microgrids, either grid-connected or islanded, provide a key solution for integrating RERs, load demand flexibility, and energy storage systems within this framework. Nonetheless, renewable energy resources, such as solar and wind energy, can be extremely stochastic as they are weather dependent. These resources coupled with load demand uncertainties lead to random variations on both the generation and load sides, thus challenging optimal energy management. This thesis develops an optimal energy management system (EMS) for a grid-tied solar PV-battery microgrid. The goal of the EMS is to obtain the minimum operational costs (cost of power exchange with the utility and battery wear cost) while still considering network constraints, which ensure grid violations are avoided. A reinforcement learning (RL) approach is proposed to minimize the operational cost of the microgrid under this stochastic setting. RL is a reward-motivated optimization technique derived from how animals learn to optimize their behaviour in new environments. Unlike other conventional model-based optimization approaches, RL doesn't need an explicit model of the optimization system to get optimal solutions. The EMS is modelled as a Markov Decision Process (MDP) to achieve optimality considering the state, action, and reward function. The feasibility of two RL algorithms, namely, conventional Q-learning algorithm and deep Q network algorithm, are developed, and their efficacy in performing optimal energy management for the designed system is evaluated in this thesis. First, the energy management problem is expressed as a sequential decision-making process, after which two algorithms, trading, and non-trading algorithm, are developed. In the trading algorithm case, excess microgrid's energy can be sold back to the utility to increase revenue, while in the latter case constraining rules are embedded in the designed EMS to ensure that no excess energy is sold back to the utility. Then a Q-learning algorithm is developed to minimize the operational cost of the microgrid under unknown future information. Finally, to evaluate the performance of the proposed EMS, a comparison study between a trading case EMS model and a non-trading case is performed using a typical commercial load curve and PV generation profile over a 24- hour horizon. Numerical simulation results indicated that the algorithm learned to select an optimized energy schedule that minimizes energy cost (cost of power purchased from the utility based on the time-varying tariff and battery wear cost) in both summer and winter case studies. However, comparing the non-trading EMS to the trading EMS model operational costs, the latter one decreased cost by 4.033% in the summer season and 2.199% in the winter season. Secondly, a deep Q network (DQN) method that uses recent learning algorithm enhancements, including experience replay and target network, is developed to learn the system uncertainties, including load demand, grid prices and volatile power supply from the renewables solve the optimal energy management problem. Unlike the Q-learning method, which updates the Q-function using a lookup table (which limits its scalability and overall performance in stochastic optimization), the DQN method uses a deep neural network that approximates the Q- function via statistical regression. The performance of the proposed method is evaluated with differently fluctuating load profiles, i.e., slow, medium, and fast. Simulation results substantiated the efficacy of the proposed method as the algorithm was established to learn from experience to raise the battery state of charge and optimally shift loads from a one-time instance, thus supporting the utility grid in reducing aggregate peak load. Furthermore, the performance of the proposed DQN approach was compared to the conventional Q-learning algorithm in terms of achieving a minimum global cost. Simulation results showed that the DQN algorithm outperformed the conventional Q-learning approach, reducing system operational costs by 15%, 24%, and 26% for the slow, medium, and fast fluctuating load profiles in the studied cases

Microgrids/Nanogrids Implementation, Planning, and Operation

Today’s power system is facing the challenges of increasing global demand for electricity, high-reliability requirements, the need for clean energy and environmental protection, and planning restrictions. To move towards a green and smart electric power system, centralized generation facilities are being transformed into smaller and more distributed ones. As a result, the microgrid concept is emerging, where a microgrid can operate as a single controllable system and can be viewed as a group of distributed energy loads and resources, which can include many renewable energy sources and energy storage systems. The energy management of a large number of distributed energy resources is required for the reliable operation of the microgrid. Microgrids and nanogrids can allow for better integration of distributed energy storage capacity and renewable energy sources into the power grid, therefore increasing its efficiency and resilience to natural and technical disruptive events. Microgrid networking with optimal energy management will lead to a sort of smart grid with numerous benefits such as reduced cost and enhanced reliability and resiliency. They include small-scale renewable energy harvesters and fixed energy storage units typically installed in commercial and residential buildings. In this challenging context, the objective of this book is to address and disseminate state-of-the-art research and development results on the implementation, planning, and operation of microgrids/nanogrids, where energy management is one of the core issues

Integrating Hybrid Off-grid Systems with Battery Storage: Key Performance Indicators

A clear opportunity exists for the integration of Battery Energy Storage Systems (BESS) in hybrid off-grid applications, i.e., isolated grids with renewable sources (e.g. PV, wind) and small-scale diesel generators. In these applications, renewable sources have the potential to reduce petroleum derivatives consumption (diesel, lubricants, etc.) and reduce Greenhouse Gases emissions. Therefore, in recent years the changes in the economics of renewables and, particularly, PV sources, have led to their integration with diesel generators in order to reduce the Operational Expenditure (OPEX) of off-grid systems. BESS present the capability of maximizing the integration of renewable energy and, consequently, further offset the use of diesel-fired generating units. The purpose of this work is twofold: First, the objective is to identify the Key Performance Indicators (KPI) for assessing the integration of Hybrid Off-grid Systems with BESS. Second, these KPI, reflecting the potential impacts of battery Storage within an Hybrid Off-grid system, will enable the assessment of the business case of the BESS integration

Techno-economic impact of single feeder: multiple microgrids on power utility companies.

Masters Degree. University of KwaZulu-Natal, Durban.Abstract available in the PDFList of Figures on page numbers ix-xi