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

Power Flow Control of the Grid-Integrated Hybrid DG System using an ARFMF Optimization

A power flow control scheme for a grid-integrated Hybrid DG System (HDGS) is presented in this work, utilizing an advanced random forest algorithm combined with the moth-flame optimization (ARFMF) approach. The proposed control scheme combines the random forest algorithm (RFA) and moth-flame optimization algorithm (MFO) for consolidated execution. The random forest algorithm (RFA), an AI technique, is well-suited for nonlinear systems due to its accurate interpolation and extrapolation capabilities. It is an ensemble learning method that combines multiple decision trees to make predictions. The algorithm constructs a forest of decision trees and aggregates their predictions to produce the final output. The moth-flame optimization (MFO) process is a meta-heuristic optimization procedure inspired by the transverse orientation of moths in nature. It improves initial random solutions and converges to superior positions in the search area. Similarly, the MFO is effective in nonlinear systems as it accurately interpolates and extrapolates arbitrary information. In the proposed technique, the RFA performs the calculation process to determine precise control gains for the HDGS through online implementation based on power variation between the source side and the load side. The recommended dataset is used to implement the AI approach for online execution, reducing optimization process time. The learning process of the RFA is guided by the MFO optimization algorithm. The MFO technique defines the objective function using system information based on equal and unequal constraints, including the accessibility of renewable energy sources, power demand, and state of charge (SOC) of storage systems. Storage devices such as batteries stabilize the energy generated by renewable energy systems to maintain a constant, stable output power. The proposed model is implemented on the MATLAB/Simulink platform, and its execution is compared to previous approaches

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

Integrated design of photovoltaic power generation plant with pumped hydro storage system and agricultural facilities in Uhuelem-Amoncha African community

Seasonal and location dependence of renewable energy resources have limited their applications in power generation. Energy storage systems are promising solutions to the intermittence of renewable energy resources. Rural electricity grids are faced with economic sustainability challenges due to low power demand and poverty. As countries hopefully pass through various stages of development, their needs change. The electricity needs of developing countries surely differ from those of developed economies. Most of the global population without access to electricity, and all the consequences of it, is found in developing countries. Energy access is undoubtedly a significant catalyst for development. Developed countries mainly require technologies to ensure energy security, resilience, and occasionally emission control. Therefore, microgrids are emerging technologies capable of supporting the diverse needs of various stages of development. For example, a rural grid design around economic drivers like agriculture and micro industries can mitigate poverty and improve economic sustainability of rural grids. This study presents an Integrated Design of Photovoltaic Power Generation Plant with Pumped Hydro Storage System and Agricultural Facilities in Uhuelem-Amoncha African Community. The design explored the natural availability of water body in an elevated settlement area that offers a natural storage height for hydro energy storage. HOMER (Hybrid Optimization of Multiple Energy Resources) software was deployed to optimize the design. The designed photovoltaic power generation plant has a nominal capacity of 221 kW. The simulated results show the power supply probability of the plant as 99.9%. The cost of energy (COE) offered by the design is 0.456 [US$/kWh] which is 82% lower than the current cost of energy in the project community based on generation through petrol generators. The System has 100% renewable energy penetration. The plant is designed to power 50 households with a daily domestic energy consumption of 4.46 [kWh] each. The plant capacity also covers the irrigation water requirement of 50 acres of corn farms. A total of 100 units of designed intelligent pest control system will also be powered by the plant. A community refrigeration scheme of 27 [m3] equivalent volume is part of the plant design load. The benefits from the irrigation, water supply, pest control and refrigeration scheme will enhance the community’s socio-economic development and sustain the investment. Quantifying the integral socio-economic and environmental benefits is a subject of a future research

Optimal Control of Hybrid Systems and Renewable Energies

This book is a collection of papers covering various aspects of the optimal control of power and energy production from renewable resources (wind, PV, biomass, hydrogen, etc.). In particular, attention is focused both on the optimal control of new technologies and on their integration in buildings, microgrids, and energy markets. The examples presented in this book are among the most promising technologies for satisfying an increasing share of thermal and electrical demands with renewable sources: from solar cooling plants to offshore wind generation; hybrid plants, combining traditional and renewable sources, are also considered, as well as traditional and innovative storage systems. Innovative solutions for transportation systems are also explored for both railway infrastructures and advanced light rail vehicles. The optimization and control of new solutions for the power network are addressed in detail: specifically, special attention is paid to microgrids as new paradigms for distribution networks, but also in other applications (e.g., shipboards). Finally, optimization and simulation models within SCADA and energy management systems are considered. This book is intended for engineers, researchers, and practitioners that work in the field of energy, smart grid, renewable resources, and their optimization and control

Towards the next generation of smart grids: semantic and holonic multi-agent management of distributed energy resources

The energy landscape is experiencing accelerating change; centralized energy systems are being decarbonized, and transitioning towards distributed energy systems, facilitated by advances in power system management and information and communication technologies. This paper elaborates on these generations of energy systems by critically reviewing relevant authoritative literature. This includes a discussion of modern concepts such as ‘smart grid’, ‘microgrid’, ‘virtual power plant’ and ‘multi-energy system’, and the relationships between them, as well as the trends towards distributed intelligence and interoperability. Each of these emerging urban energy concepts holds merit when applied within a centralized grid paradigm, but very little research applies these approaches within the emerging energy landscape typified by a high penetration of distributed energy resources, prosumers (consumers and producers), interoperability, and big data. Given the ongoing boom in these fields, this will lead to new challenges and opportunities as the status-quo of energy systems changes dramatically. We argue that a new generation of holonic energy systems is required to orchestrate the interplay between these dense, diverse and distributed energy components. The paper therefore contributes a description of holonic energy systems and the implicit research required towards sustainability and resilience in the imminent energy landscape. This promotes the systemic features of autonomy, belonging, connectivity, diversity and emergence, and balances global and local system objectives, through adaptive control topologies and demand responsive energy management. Future research avenues are identified to support this transition regarding interoperability, secure distributed control and a system of systems approach

Optimal generation scheduling for renewable microgrids using hydrogen storage systems

The topic of this thesis is the development of a tool for an optimal energy management strategy (EMS) of the generators and energy storage systems constituent microgrids, both grid-connected or isolated (stand-alone power system) powered by Renewable Energy Sources (RES). In particular, a novel control system is designed based on the resolution of the unit commitment problem. For each time step, the proposed control system compares the expected power produced by the renewable generators with the expected load demand and determines the scheduling of the different energy storage devices and generators for the next few hours, which minimizes the operating cost of the overall microgrid. To take into account for forecasting uncertainties, the generation of the different scenarios is carried out through a discretization of the probability distribution function of the forecasting errors for wind speed, solar radiation and load requests by a set of finite states. A set of various scenarios are therefore analyzed and compared by the control system to find the minimum operating costs. The proposed algorithm is firstly applied to a microgrid at LABH2FER (Sardegna Ricerche, Italy). Since the microgrid is under construction, the expected performance is evaluated through a simulation modeling, implemented in Matlab-Simulink. Furthermore, in order to highlight the benefits of including weather forecasts and operating costs in the EMS, a comparative analysis with a simpler EMS based on control states of storage devices is carried out. The results of the comparative study demonstrate that a reduction of almost 5-10% in the annual operating costs and energy losses is achieved thanks to the implementation of the proposed control system. Moreover, the proposed control strategy is implemented and tested to a microgrid present at the University of Seville. Experimental results demonstrate the feasibility and the actual functionality of the control system. Additional benefits are also observed, such as the reduction in power exchanged with the upstream grid thanks to a better management of the storage systems

The determinants of decentralised photovoltaic (PV) adoption in urban Nigeria and a verified model for rapid diffusion

Microgeneration technologies like residential solar photovoltaic (PV) systems have been shown to have immense potential for energy security and climate change mitigation. As a way of helping to resolve the decades-long power challenge in Nigeria, this study investigated the barriers to, and motives for, domestic PV adoption in Nigeria. It also assessed whether household PV can lead to increased energy use efficiency and examined the role of Government incentives towards large-scale uptake and diffusion. Adoption and innovation diffusion theories, willingness-to-pay (WTP), coproduction and self-help concepts were employed. Results were analysed using mainly Lagos State household data, collected through questionnaire surveys and interviews. Findings from correlation and logistic regression revealed the major barriers as high capital costs, lack of finance and low awareness. Field survey analysis established the key motives for uptake as power outages, cost-savings, including generator use fuel fraud and access to finance. It also showed that post-PV, adopting households engaged in more energy efficient practices. From this data the PV efficiency cycle was developed to demonstrate how energy conservation occurred. Empirical evidence from the surveys, interviews and LCOE calculations were used to design a verified model for rapid PV diffusion. This decision-making tool can be used by the Government, policymakers, PV designers, SMEs and households for choosing an appropriately-sized module. The results point to the need for regulatory and political intervention. Effective PV awareness creation campaigns and promotional strategies would also be necessary in the changing face of electricity supply in Nigeria

Komponentenbasierte dynamische Modellierung von Energiesystemen und Energiemanagement-Strategien für ein intelligentes Stromnetz im Heimbereich

The motivation of this work is to present an energy cost reduction concept in a home area power network (HAPN) with intelligent generation and flexible load demands. This study endeavors to address the energy management system (EMS) and layout-design challenges faced by HAPN through a systematic design approach. The growing demand for electricity has become a significant burden on traditional power networks, prompting power engineers to seek ways to improve their efficiency. One such solution is to integrate dispersed generation sources, such as photovoltaic (PV) and storage systems, with an appropriate control mechanism at the distribution level. In recent years, there has been a significant increase in interest in the installation of PV-Battery systems, due to their potential to reduce carbon emissions and lower energy costs. This research proposes an optimal economic power dispatch strategy using Model Predictive Control (MPC) to enhance the overall performance of HAPN. A hybrid AC/DC microgrid concept is proposed to address the control choices made by the appliance scheduling and hybrid switching approaches based on a linear programming optimization framework. The suggested optimization criteria improve consumer satisfaction, minimize grid disconnections, and lower overall energy costs by deploying inexpensive clean energy generation and control. Various examples from actual case study demonstrate the use of the established EMS and design methodology.Die Motivation dieser Arbeit besteht darin, ein Konzept zur Senkung der Energiekosten in einem Heimnetzwerk (HAPN) mit intelligenter Erzeugung und exiblen Lastanforderungen vorzustellen. Im Rahmen dieser Forschungsarbeit wird ein Entwurf für ein HAPN entwickelt, indem das Energiemanagementsystem (EMS) und der Entwurf des Layouts auf der Grundlage des Systemmodells und der betrieblichen Anforderungen gelöst werden. Die steigende Nachfrage nach Elektrizität ist für traditionelle Stromnetze kostspielig und infrastrukturintensiv. Daher konzentrieren sich Energietechniker darauf, die Effizienz der derzeitigen Netze zu erhöhen. Dies kann durch die Integration verteilter Erzeugungsanlagen (z. B. Photovoltaik (PV), Speicher) mit einem geeigneten Kontrollmechanismus für das Energiemanagement auf der Verteilungsseite erreicht werden. Darüber hinaus hat das Interesse an der Installation von PV-Batterie-basierten Systemen aufgrund der Reduzierung der CO2-Emissionen und der Senkung der Energiekosten erheblich zugenommen. Es wird eine optimale wirtschaftliche Strategie für den Energieeinsatz unter Verwendung einer modellprädiktiven Steuerung (MPC) entwickelt. Es wird zudem ein hybrides AC/DC-Microgrid-Konzept vorgeschlagen, um die Steuerungsentscheidungen, die von den Ansätzen der Geräteplanung und der hybriden Umschaltung getroffen werden, auf der Grundlage eines linearen Programmierungsoptimierungsrahmens zu berücksichtigen. Die vorgeschlagenen Optimierungskriterien verbessern die Zufriedenheit der Verbraucher, minimieren Netzabschaltungen und senken die Gesamtenergiekosten durch den Einsatz von kostengünstiger und sauberer Energieerzeugung. Verschiedene Beispiele aus einer Fallstudie demonstrieren den Einsatz des entwickelten EMS und der Entwurfsmethodik

Energy Management of Grid-Connected Microgrids, Incorporating Battery Energy Storage and CHP Systems Using Mixed Integer Linear Programming

In this thesis, an energy management system (EMS) is proposed for use with battery energy storage systems (BESS) in solar photovoltaic-based (PV-BESS) grid-connected microgrids and combined heat and power (CHP) applications. As a result, the battery's charge/discharge power is optimised so that the overall cost of energy consumed is minimised, considering the variation in grid tariff, renewable power generation and load demand. The system is modelled as an economic load dispatch optimisation problem over a 24-hour time horizon and solved using mixed integer linear programming (MILP) for the grid-connected Microgrid and the CHP application. However, this formulation requires information about the predicted renewable energy power generation and load demand over the next 24 hours. Therefore, a long short-term memory (LSTM) neural network is proposed to achieve this. The receding horizon (RH) strategy is suggested to reduce the impact of prediction error and enable real-time implementation of the energy management system (EMS) that benefits from using actual generation and demand data in real-time. At each time-step, the LSTM predicts the generation and load data for the next 24 h. The dispatch problem is then solved, and the real-time battery charging or discharging command for only the first hour is applied. Real data are then used to update the LSTM input, and the process is repeated. Simulation results using the Ushant Island as a case study show that the proposed online optimisation strategy outperforms the offline optimisation strategy (with no RH), reducing the operating cost by 6.12%. The analyses of the impact of different times of use (TOU) and standard tariff in the energy management of grid-connected microgrids as it relates to the charge/discharge cycle of the BESS and the optimal operating cost of the Microgrid using the LSTM-MILP-RH approach is evaluated. Four tariffs UK tariff schemes are considered: (1) Residential TOU tariff (RTOU), (2) Economy seven tariff (E7T), (3) Economy ten tariff (E10T), and (4) Standard tariff (STD). It was found that the RTOU tariff scheme gives the lowest operating cost, followed by the E10T tariff scheme with savings of 63.5% and 55.5%, respectively, compared to the grid-only operation. However, the RTOU and E10 tariff scheme is mainly used for residential applications with the duck curve load demand structure. For community grid-connected microgrid applications except for residential-only communities, the E7T and STD, with 54.2% and 39.9%, respectively, are the most likely options offered by energy suppliers. The use of combined heat and power (CHP) systems has recently increased due to their high combined efficiency and low emissions. Using CHP systems in behind-the-meter applications, however, can introduce some challenges. Firstly, the CHP system must operate in load-following mode to prevent power export to the grid. Secondly, if the load drops below a predefined threshold, the engine will operate at a lower temperature and hence lower efficiency, as the fuel is only half-burnt, creating significant emissions. The aforementioned issues may be solved by combining CHP with a battery energy storage system. However, the dispatch of CHP and BESS must be optimised. Offline optimisation methods based on load prediction will not prevent power export to the grid due to prediction errors. Therefore, a real-time EMS using a combination of LSTM neural networks, MILP, and RH control strategy is proposed. Simulation results show that the proposed method can prevent power export to the grid and reduce the operational cost by 8.75% compared to the offline method. The finding shows that the BESS is a valuable asset for sustainable energy transition. However, they must be operated safely to guarantee operational cost reduction and longer life for the BESS