Data-Intensive Computing in Smart Microgrids

Microgrids have recently emerged as the building block of a smart grid, combining distributed renewable energy sources, energy storage devices, and load management in order to improve power system reliability, enhance sustainable development, and reduce carbon emissions. At the same time, rapid advancements in sensor and metering technologies, wireless and network communication, as well as cloud and fog computing are leading to the collection and accumulation of large amounts of data (e.g., device status data, energy generation data, consumption data). The application of big data analysis techniques (e.g., forecasting, classification, clustering) on such data can optimize the power generation and operation in real time by accurately predicting electricity demands, discovering electricity consumption patterns, and developing dynamic pricing mechanisms. An efficient and intelligent analysis of the data will enable smart microgrids to detect and recover from failures quickly, respond to electricity demand swiftly, supply more reliable and economical energy, and enable customers to have more control over their energy use. Overall, data-intensive analytics can provide effective and efficient decision support for all of the producers, operators, customers, and regulators in smart microgrids, in order to achieve holistic smart energy management, including energy generation, transmission, distribution, and demand-side management. This book contains an assortment of relevant novel research contributions that provide real-world applications of data-intensive analytics in smart grids and contribute to the dissemination of new ideas in this area

Control of Energy Storage

Energy storage can provide numerous beneficial services and cost savings within the electricity grid, especially when facing future challenges like renewable and electric vehicle (EV) integration. Public bodies, private companies and individuals are deploying storage facilities for several purposes, including arbitrage, grid support, renewable generation, and demand-side management. Storage deployment can therefore yield benefits like reduced frequency fluctuation, better asset utilisation and more predictable power profiles. Such uses of energy storage can reduce the cost of energy, reduce the strain on the grid, reduce the environmental impact of energy use, and prepare the network for future challenges. This Special Issue of Energies explore the latest developments in the control of energy storage in support of the wider energy network, and focus on the control of storage rather than the storage technology itself

Efficiency and Sustainability of the Distributed Renewable Hybrid Power Systems Based on the Energy Internet, Blockchain Technology and Smart Contracts-Volume II

The climate changes that are becoming visible today are a challenge for the global research community. In this context, renewable energy sources, fuel cell systems, and other energy generating sources must be optimally combined and connected to the grid system using advanced energy transaction methods. As this reprint presents the latest solutions in the implementation of fuel cell and renewable energy in mobile and stationary applications, such as hybrid and microgrid power systems based on the Energy Internet, Blockchain technology, and smart contracts, we hope that they will be of interest to readers working in the related fields mentioned above

Optimization Methods Applied to Power Systems Ⅱ

Electrical power systems are complex networks that include a set of electrical components that allow distributing the electricity generated in the conventional and renewable power plants to distribution systems so it can be received by final consumers (businesses and homes). In practice, power system management requires solving different design, operation, and control problems. Bearing in mind that computers are used to solve these complex optimization problems, this book includes some recent contributions to this field that cover a large variety of problems. More specifically, the book includes contributions about topics such as controllers for the frequency response of microgrids, post-contingency overflow analysis, line overloads after line and generation contingences, power quality disturbances, earthing system touch voltages, security-constrained optimal power flow, voltage regulation planning, intermittent generation in power systems, location of partial discharge source in gas-insulated switchgear, electric vehicle charging stations, optimal power flow with photovoltaic generation, hydroelectric plant location selection, cold-thermal-electric integrated energy systems, high-efficiency resonant devices for microwave power generation, security-constrained unit commitment, and economic dispatch problems

Model Predictive based load frequency control studies in a deregulated environment

A fundamental objective in power system operations is to ensure reliablity and quality supply, and one key action that aids the accomplishment of this objective is the load frequency control (LFC). Primarily, LFC is an automatic action that aims to restore system frequency and net tie line power between a control area (CA) and its neighbours to their scheduled values; these quantities deviate when there is an imbalance between active power demand and supply in a synchrononus interconnection. This thesis aims to investigate a model predictive control (MPC) technique for LFC problems in a deregulated power system environment which has become a challenging task. In deregulated power interconnections, generation companies (GenCos) and distribution companies (DisCos) exist in each CA, and a transmission system operator (TSO) in each area is responsible for grid reliability. Each TSO handles LFC in its CA and ensures that market participants (GenCos and DisCos) in other CAs have an unbiased and open access to its network. As a result, there has been a rise in cross-border transac- tions between GenCos and DisCos for bulk energy and load matching (LM) and consequently large frequency fluctuations recently. DisCos can participate in LFC by making bilateral LM contracts with GenCos. An extensive review of the LFC literature, in terms of strengths and weaknesses of different control techniques, is presented to identify the key gaps. The review reveals that MPC can bring some benefits in the deregulated environment but its strengths are underexploited. Beginning with a small-scale system to provide insights into deregulated system modelling and predictive control design, a centralised MPC (CMPC)-based LFC scheme is proposed for a 2-area deregulated power system with measured (contracted) and unmeasured (uncontracted) load changes, where the areas are assumed to equally rated. The 2-area deregulated system is developed by incorporating bilateral LM contracts in the well known traditional LFC model as a new set of information. It is assumed that DisCos handle contracted load changes via bilateral LM contracts with GenCos and a TSO handles any variations outside the LM con- tracts (uncontracted) via a supplementary control scheme which represents the CMPC. The CMPC algorithm is developed as a tracking one, with an observer to provide estimates of the system states and uncontracted load changes. Also, input and incremental state constraints, which depict limits on LFC control efforts and generation rate constraints (GRC) respectively, are considered. A simulation comparison of the proposed CMPC solution and optimal linear quadratic regulator (LQR) demonstrates the efficacy of CMPC. Developing deregulated LFC models for larger systems with complex topologies and a large number of CAs/market participants could be laborious. Therefore, a novel generalised modelling framework for deregulated LFC is further proposed. The key benefits of the generalised framework is that it provides a relatively easy and systematic procedure to develop deregulated LFC benchmark systems irrespective of the interconnection size, topology and number of market participants. It also offers the flexibility of accommodating LFC studies where CAs have either equal (often assumed) or unequal (more pragmatic) rated capacities. A 7-area deregulated benchmark model is developed from the generalised framework to illustrate its usage and significance, and the importance of incorporating area rated capacities is demonstrated via simulations. In addition, a 4-area benchmark model is developed to provide a reader with more insight into how the generalised formulation can be applied to develop LFC models for an arbitrary network. Furthermore, to demonstrate the scalability of an MPC design procedure, the CMPC proposed previously is extended to examine the LFC problem of the 7-area system. Key novelties here are CAs are assumed to have unequal rated capacities, some GenCos do not participate in supplementary control, and the control input to each GenCo is computed separately rather than a single lumped input for each CA which is the norm in previous deregulated LFC studies. The separate control inputs is to ensure that the input constraints of each GenCo is accounted for in the CMPC in addition to their GRCs and this is achieved by incorporating the area participation factors of the GenCos explicitly in the CMPC cost function. A test conducted on the 7-area benchmark confirms the benefits of this new approach. CMPC shows great potential for deregulated LFC in terms of multiple inputs coordination, effective disturbance rejection and constraints handling; however it is unrealistic for practical interconnections were CAs are operated by different organisations and have large geographical separations. This limitation is addressed by investigating a distributed MPC (DMPC) technique for rejecting incremental load changes, convenient for a finite number of control areas (subsystems), and therefore represents a more practical control architecture for LFC in multi-area systems. The proposed DMPC is non-cooperative and developed to operate using output feedback, where distributed observers using local measurements are developed to provide uncontracted load changes and subsystem states’ estimates to local MPCs. Moreover, the DMPC, unlike other non-cooperative schemes, is simple and devoid of extensive offline parameter tuning. Using the 4-area and the 7-area benchmarks models developed as test systems for the proposed DMPC, some comparisons of simulations results, regulation cost and discussions are provided between the proposed DMPC and alternative MPC schemes

Practical Implementation of Hybrid Energy Systems for Small Loads in Rural South Africa

DissertationHybrid renewable energy systems (HRESs), are alternative off-grid methods of generating power to remote rural areas, where power lines are not economically viable. Most of the research studies on renewable hybrid systems or microgrids (MGs) in South Africa, focus mainly on the optimal sizing and optimal control of different systems, by making use of renewable energy simulation softwares, however, there is a lack of research carried out on the implementation of these hybrid systems in real time. The aim is to develop a real time control method for an isolated hybrid system submitted to a variable load, as well as resources. The first step towards achieving this aim, was to critically review available published research works, to describe recent developments in improving the optimum operating concept of microgrid controllers for stand-alone or grid-connected systems. Secondly, to investigate any real-time implementation established by either hierarchical or distributed control. Then to, analyze their reliability and functionality in practical set up of the controller, in managing power in the system to the variable load. The study provided a brief overview of microgrid prototype systems, microgrid controls, operating modes and multi-DER microgrid types built into a hybrid system, which introduces a number of strategies or techniques for managing remote rural application prototypes in an isolated or grid-connected system. However, hierarchical control was found to be more appropriate for large microgrids with multiple types of distributed energy resources (DERs), compared to distributed control, particularly when combined with energy storage systems (ESSs), in isolated mode. The rising of hybrid system controllers in real-time renewable energy for the optimum energy management system (EMS), required the design of a real-time controller to operate the entire system in real time. Increasing popularity of renewable energy (RE) has a control strategy that determined the overall efficiency of the hybrid system (HS), although the energy management system of these systems is particularly complex to be managed. The study's main contribution is to investigate the feasible controller and, later, to present an advanced control strategy for managing and controlling the flow of hybrid renewable energy with a diesel generator (DG) and battery (BT) as a backup in a rural application of SA. EMS would be implemented, using a fuzzy logic controller (FLC) in MATLAB / SIMULINK. This study analysed input and output variables for the design of a controller, with a set of rules and a three-dimension (3D) surface. Simulation results of related studies with different objectives were analysed, with the aim of sussing out an appropriate controller for the current study. Arduino Mega was used for coding and uploaded to the implementation of practical implementation of the study. The system operated successfully by supplying the load. This study finally answered the question of the feasibility of the controller in real-time applications

Advanced analysis of load management and environment friendly energy technologies integration in electric power system

The Commonwealth Scholarship Commission (CSC) in the United Kingdom places its primary emphasis on six distinct development-related themes namely, science and technology for development, strengthening health systems, promoting global prosperity, strengthening resilience and response to crises, access, inclusion, and opportunity, and strengthening global peace, security, and governance. My motivation as a Commonwealth scholar, comes from the discussion surrounding the application of science and technology for the development, which is related to the seventh among 17 sustainable development goals (SDGs). The endorsed goal aims at ensuring that all people have access to energy that is both clean and affordable. In this context, my research focuses on the advanced analysis of load management and energy conservation strategies in developing countries, with Rwanda as its primary focus. Firstly, this research work supports the development of Rwanda's energy system and addresses gaps in the existing energy data by proposing a set of Future Energy Scenarios (FES). The developed FES are used to estimate the energy consumption and generation capacity until 2050. Secondly, this research analyses the impact of technologies that are adopted in the developed FES on the Rwanda’s power system. As Electric Vehicles (EVs) are highlighted as an important component in decarbonisation of transport, the study analyses the EVs deployment into the country’s transport and electricity networks. Another challenge that this research is addressing, is the impact the proposed FESs imposes on the power system inertia constant as a result of the integration of renewable energy sources. This is because conventional power plants are replaced by renewable generation (e.g., photovoltaics considered in this study) that contribute to the reduction of power system inertia. In addition to the feasibility study for the deployment of EVs in the country’s transport and electricity networks, this research also developed a methodology to estimates the inertia constant for three different periods in future, namely, 2025, 2035 and 2050 based on the produced FESs for Rwandan’s power system. Furthermore, the research evaluates the frequency response dynamics for each scenario. Results show that the highest progression in renewable energy sources penetration results in a larger reduction in the system inertia constant. The largest frequency drop was observed during the high progression scenario in the year 2050 where the PV generation and imported power from neighbouring countries through interconnectors is expected to reach more than 30% of the total installed capacity. Finally, to mitigate this large drop in frequency, the work proposed a method for stabilising grid frequency by considering demand flexibility. With the help of the load aggregator, prosumers receive price incentive signals based on their energy consumption and prepare them for their participation in grid frequency stabilisation. By considering the operation of a wide range of renewable energy sources and load management system, the study investigates the reduction of the total reliance on electricity from the grid, in day-ahead and real-time energy markets, while also balancing an anticipated load. The proposed control framework considers the estimated power availability and it is used in conjunction with the participation of a load aggregator for contributing to the stabilisation of grid frequency

Optimisation, Optimal Control and Nonlinear Dynamics in Electrical Power, Energy Storage and Renewable Energy Systems

The electrical power system is undergoing a revolution enabled by advances in telecommunications, computer hardware and software, measurement, metering systems, IoT, and power electronics. Furthermore, the increasing integration of intermittent renewable energy sources, energy storage devices, and electric vehicles and the drive for energy efficiency have pushed power systems to modernise and adopt new technologies. The resulting smart grid is characterised, in part, by a bi-directional flow of energy and information. The evolution of the power grid, as well as its interconnection with energy storage systems and renewable energy sources, has created new opportunities for optimising not only their techno-economic aspects at the planning stages but also their control and operation. However, new challenges emerge in the optimization of these systems due to their complexity and nonlinear dynamic behaviour as well as the uncertainties involved.This volume is a selection of 20 papers carefully made by the editors from the MDPI topic “Optimisation, Optimal Control and Nonlinear Dynamics in Electrical Power, Energy Storage and Renewable Energy Systems”, which was closed in April 2022. The selected papers address the above challenges and exemplify the significant benefits that optimisation and nonlinear control techniques can bring to modern power and energy systems

Advances in Optimization and Nonlinear Analysis

The present book focuses on that part of calculus of variations, optimization, nonlinear analysis and related applications which combines tools and methods from partial differential equations with geometrical techniques. More precisely, this work is devoted to nonlinear problems coming from different areas, with particular reference to those introducing new techniques capable of solving a wide range of problems. The book is a valuable guide for researchers, engineers and students in the field of mathematics, operations research, optimal control science, artificial intelligence, management science and economics