Optimal Siting and Sizing of Solar Photovoltaic Distributed Generation to Minimize Loss, Present Value of Future Asset Upgrades and Peak Demand Costs on a Real Distribution Feeder

The increasing penetration of distributed generation (DG) in power distribution systems presents technical and economic benefits as well as integration challenges to utility engineers. Governments are beginning to acknowledge DG as an economically viable alternative to deferring investment at generation, transmission and distribution levels, meeting demand growth and improving distribution network performance and security. DG technology is rapidly maturing in Ontario due to government economic incentives promoting connection, specifically, the Ontario’s Feed-In-Tariff (FIT) Program. Optimal sizing and siting of DG is well researched, traditionally studying the technical impact on distribution system such as real power loss reduction and voltage profile improvement. Equally common objectives studied are the economics of DG installation which are useful for the developer when deciding when and where to install. Although DG represents a “non-wires” solution to network asset reinforcement, the direct economic benefit to the host utility from promoting DG uptake is not fully understood by utility planners and asset managers. Some DG based asset reinforcement deferral work has been performed in the UK and Italy but is mainly at the transmission level and is not part of an overall strategy that could be applied by a utility. This research presents a comprehensive three stage technique: optimal siting, optimal sizing and financial evaluation of cost savings over a defined planning period to quantify the economic benefit to a Local Distribution Company (LDC) of solar photovoltaic (PV) DG connections on an actual distribution feeder. Optimal sites for PV DG are determined by applying the power loss sensitivity factor method to the test feeder. The objective functions used to determine cost savings consist of loss minimization, asset investment deferral, and peak demand reduction to identify an optimal DG penetration limit. Furthermore, a utility planner can identify an optimal DG penetration limit, encourage uptake at preferred locations that would benefit the LDC, and use the positive impact of DG at existing locations as part of an asset management strategy to prioritize and schedule future asset reinforcement upgrades

Distributed Power Generation Scheduling, Modelling and Expansion Planning

Distributed generation is becoming more important in electrical power systems due to the decentralization of energy production. Within this new paradigm, new approaches for the operation and planning of distributed power generation are yet to be explored. This book deals with distributed energy resources, such as renewable-based distributed generators and energy storage units, among others, considering their operation, scheduling, and planning. Moreover, other interesting aspects such as demand response, electric vehicles, aggregators, and microgrid are also analyzed. All these aspects constitute a new paradigm that is explored in this Special Issue

Planning optimal load distribution and maximum renewable energy from wind power on a radial distribution system

Doctor of PhilosophyElectrical and Computer EngineeringRuth D. MillerOptimizing renewable distributed generation in distribution systems has gained popularity with changes in federal energy policies. Various studies have been reported in this regard and most of the studies are based on optimum wind and/or solar generation planning in distribution system using various optimization techniques such as analytical, numerical, and heuristic. However, characteristics such as high energy density, relatively lower footprint of land, availability, and local reactive power compensation ability, have gained increased popularity for optimizing distributed wind generation (DWG) in distribution systems. This research investigated optimum distributed generation planning (ODGP) using two primary optimization techniques: analytical and heuristic. In first part of the research, an analytical optimization method called “Combined Electrical Topology (CET)” was proposed in order to minimize the impact of intentional structural changes in distribution system topology, in distributed generation/ DWG placement. Even though it is still rare, DWG could be maximized to supply base power demand of three-phase unbalanced radial distribution system, combined with distributed battery energy storage systems (BESS). In second part of this research the usage of DWG/BESS as base power generation, and to extend the ability to sustain the system in a power grid failure for a maximum of 1.5 hours was studied. IEEE 37-node, three-phase unbalanced radial distribution system was used as the test system to optimize wind turbines and sodium sulfide (NaS) battery units with respect to network real power losses, system voltage profile, DWG/BESS availability and present value of cost savings. In addition, DWG’s ability to supply local reactive power in distribution system was also investigated. Model results suggested that DWG/NaS could supply base power demand of a threephase unbalanced radial distribution system. In addition, DWG/NaS were able to sustain power demand of a three-phase unbalanced distribution system for 1.5 hours in the event of a power grid failure

Interdependence between transportation system and power distribution system: a comprehensive review on models and applications

The rapidly increasing penetration of electric vehicles in modern metropolises has been witnessed during the past decade, inspired by financial subsidies as well as public awareness of climate change and environment protection. Integrating charging facilities, especially high-power chargers in fast charging stations, into power distribution systems remarkably alters the traditional load flow pattern, and thus imposes great challenges on the operation of distribution network in which controllable resources are rare. On the other hand, provided with appropriate incentives, the energy storage capability of electric vehicle offers a unique opportunity to facilitate the integration of distributed wind and solar power generation into power distribution system. The above trends call for thorough investigation and research on the interdependence between transportation system and power distribution system. This paper conducts a comprehensive survey on this line of research. The basic models of transportation system and power distribution system are introduced, especially the user equilibrium model, which describes the vehicular flow on each road segment and is not familiar to the readers in power system community. The modelling of interdependence across the two systems is highlighted. Taking into account such interdependence, applications ranging from long-term planning to short-term operation are reviewed with emphasis on comparing the description of traffic-power interdependence. Finally, an outlook of prospective directions and key technologies in future research is summarized.fi=vertaisarvioitu|en=peerReviewed

Wind turbine blade geometry design based on multi-objective optimization using metaheuristics

Abstract: The application of Evolutionary Algorithms (EAs) to wind turbine blade design can be interesting, by reducing the number of aerodynamic-to-structural design loops in the conventional design process, hence reducing the design time and cost. Recent developments showed satisfactory results with this approach, mostly combining Genetic Algorithms (GAs) with the Blade Element Momentum (BEM) theory. The general objective of the present work is to define and evaluate a design methodology for the rotor blade geometry in order to maximize the energy production of wind turbines and minimize the mass of the blade itself, using for that purpose stochastic multi-objective optimization methods. Therefore, the multi-objective optimization problem and its constraints were formulated, and the vector representation of the optimization parameters was defined. An optimization benchmark problem was proposed, which represents the wind conditions and present wind turbine concepts found in Brazil. This problem was used as a test-bed for the performance comparison of several metaheuristics, and also for the validation of the defined design methodology. A variable speed pitch-controlled 2.5 MW Direct-Drive Synchronous Generator (DDSG) turbine with a rotor diameter of 120 m was chosen as concept. Five different Multi-objective Evolutionary Algorithms (MOEAs) were selected for evaluation in solving this benchmark problem: Non-dominated Sorting Genetic Algorithm version II (NSGA-II), Quantum-inspired Multi-objective Evolutionary Algorithm (QMEA), two approaches of the Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D), and Multi-objective Optimization Differential Evolution Algorithm (MODE). The results have shown that the two best performing techniques in this type of problem are NSGA-II and MOEA/D, one having more spread and evenly spaced solutions, and the other having a better convergence in the region of interest. QMEA was the worst MOEA in convergence and MODE the worst one in solutions distribution. But the differences in overall performance were slight, because the algorithms have alternated their positions in the evaluation rank of each metric. This was also evident by the fact that the known Pareto Front (PF) consisted of solutions from several techniques, with each dominating a different region of the objective space. Detailed analysis of the best blade design showed that the output of the design methodology is feasible in practice, given that flow conditions and operational features of the rotor were as desired, and also that the blade geometry is very smooth and easy to manufacture. Moreover, this geometry is easily exported to a Computer-Aided Design (CAD) or Computer-Aided Engineering (CAE) software. In this way, the design methodology defined by the present work was validated

Multi-objective optimal power resources planning of microgrids with high penetration of intermittent nature generation and modern storage systems

Microgrids are self-controlled entities at the distribution voltage level that interconnect distributed energy resources (DERs) with loads and can be operated in either grid-connected or islanded mode. This type of active distribution network has evolved as a powerful concept to guarantee a reliable, efficient and sustainable electricity delivery as part of the power systems of the future. However, benefits of microgrids, such as the ancillary services (AS) provision, are not possible to be properly exploited before traditional planning methodologies are updated. Therefore, in this doctoral thesis, a named Probabilistic Multi-objective Microgrid Planning methodology with two versions, POMMP and POMMP2, is proposed for effective decision-making on the optimal allocation of DERs and topology definition under the paradigm of microgrids with capacity for providing AS to the main power grid. The methodologies are defined to consider a mixed generation matrix with dispatchable and non-dispatchable technologies, as well as, distributed energy storage systems and both conventional and power-electronic-based operation configurations. The planning methodologies are formulated based on a so-called true-multi-objective optimization problem with a configurable set of three objective functions. Accordingly, the capacity to supply AS is optimally enhanced with the maximization of the available active residual power in grid-connected operation mode; the capital, maintenance, and operation costs of microgrid are minimized, while the revenues from the services provision and participation on liberalized markets are maximized in a cost function; and the active power losses in microgrid´s operation are minimized. Furthermore, a probabilistic technique based on the simulation of parameters from their probabilistic density function and Monte Carlo Simulation is adopted to model the stochastic behavior of the non-dispatchable renewable generation resources and load demand as the main sources of uncertainties in the planning of microgrids. Additionally, POMMP2 methodology particularly enhances the proposal in POMMP by modifying the methodology and optimization model to consider the optimal planning of microgrid's topology with the allocation of DERs simultaneously. In this case, the concept of networked microgrid is contemplated, and a novel holistic approach is proposed to include a multilevel graph-partitioning technique and subsequent iterative heuristic optimization for the optimal formation of clusters in the topology planning and DERs allocation process. This microgrid planning problem leads to a complex non-convex mixed-integer nonlinear optimization problem with multiple contradictory objective functions, decision variables, and diverse constraint conditions. Accordingly, the optimization problem in the proposed POMMP/POMMP2 methodologies is conceived to be solved using multi-objective population-based metaheuristics, which gives rise to the adaptation and performance assessment of two existing optimization algorithms, the well-known Non-dominated Sorting Genetic Algorithm II (NSGAII) and the Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D). Furthermore, the analytic hierarchy process (AHP) is tested and proposed for the multi-criteria decision-making in the last step of the planning methodologies. The POMMP and POMMP2 methodologies are tested in a 69-bus and 37-bus medium voltage distribution network, respectively. Results show the benefits of an a posteriori decision making with the true-multi-objective approach as well as a time-dependent planning methodology. Furthermore, the results from a more comprehensive planning strategy in POMMP2 revealed the benefits of a holistic planning methodology, where different planning tasks are optimally and simultaneously addressed to offer better planning results.Las microrredes son entes autocontrolados que operan en media o baja tensión, interconectan REDs con las cargas y pueden ser operadas ya sea en modo conectado a la red o modo isla. Este tipo de red activa de distribución ha evolucionado como un concepto poderoso para garantizar un suministro de electricidad fiable, eficiente y sostenible como parte de los sistemas de energía del futuro. Sin embargo, para explotar los beneficios potenciales de las microrredes, tales como la prestación de servicios auxiliares (AS), primero es necesario formular apropiadas metodologías de planificación. En este sentido, en esta tesis doctoral, una metodología probabilística de planificación de microrredes con dos versiones, POMMP y POMMP2, es propuesta para la toma de decisiones efectiva en la asignación óptima de DERs y la definición de la topología de microrredes bajo el paradigma de una microrred con capacidad para proporcionar AS a la red principal. Las metodologías se definen para considerar una matriz de generación mixta con tecnologías despachables y no despachables, así como sistemas distribuidos para el almacenamiento de energía y la interconnección de recursos con o sin una interfaz basada en dispositivos de electrónica de potencia. Las metodologías de planificación se formulan sobre la base de un problema de optimización multiobjetivo verdadero con un conjunto configurable de tres funciones objetivo. Con estos se pretende optimizar la capacidad de suministro de AS con la maximización de la potencia activa residual disponible en modo conectado a la red; la minimización de los costos de capital, mantenimiento y funcionamiento de la microrred al tiempo que se maximizan los ingresos procedentes de la prestación de servicios y la participación en los mercados liberalizados; y la minimización de las pérdidas de energía activa en el funcionamiento de la microrred. Además, se adopta una técnica probabilística basada en la simulación de parámetros a partir de la función de densidad de probabilidad y el método de Monte Carlo para modelar el comportamiento estocástico de los recursos de generación renovable no despachables. Adicionalmente,la POMMP2 mejora la propuesta de POMMP modificando la metodología y el modelo de optimización para considerar simultáneamente la planificación óptima de la topología de la microrred con la asignación de DERs. Así pues, se considera el concepto de microrredes interconectadas en red y se propone un novedoso enfoque holístico que incluye una técnica de partición de gráficos multinivel y optimización iterativa heurística para la formación óptima de clusters para el planeamiento de la topología y asignación de DERs. Este problema de planificación de microrredes da lugar a un complejo problema de optimización mixto, no lineal, no convexos y con múltiples funciones objetivo contradictorias, variables de decisión y diversas condiciones de restricción. Por consiguiente, el problema de optimización en las metodologías POMMP/POMMP2 se concibe para ser resuelto utilizando técnicas multiobjetivo de optimización metaheurísticas basadas en población, lo cual da lugar a la adaptación y evaluación del rendimiento de dos algoritmos de optimización existentes, el conocido Non-dominated Sorting Genetic Algorithm II (NSGAII) y el Evolutionary Algorithm Based on Decomposition (MOEA/D). Además, se ha probado y propuesto el uso de la técnica de proceso analítico jerárquico (AHP) para la toma de decisiones multicriterio en el último paso de las metodologías de planificación. Las metodologías POMMP/POMMP2 son probadas en una red de distribución de media tensión de 69 y 37 buses, respectivamente. Los resultados muestran los beneficios de la toma de decisiones a posteriori con el enfoque de optimización multiobjetivo verdadero, así como una metodología de planificación dependiente del tiempo. Además, los resultados de la estrategia de planificación con POMMP2 revelan los beneficios de una metodología de planificación holística, en la que las diferentes tareas de planificación se abordan de manera óptima y simultánea para ofrecer mejores resultados de planificación.Línea de investigación: Planificación de redes inteligentes We thank to the Administrative Department of Science, Technology and Innovation - Colciencias, Colombia, for the granted National Doctoral funding program - 647Doctorad

Design of System Architecture and Thermal Management Components for an Underwater Energy Storage Facility

The electricity industry is currently experiencing a significant paradigm shift in managing electrical resources. With the onset of aging infrastructure and growing power demands, and the influx of intermittent renewable energy generation, grid system operators are looking towards energy storage as a solution for mitigating industry challenges. An emerging storage solution is underwater compressed air energy storage (UWCAES), where air compressors and turbo-expanders are used to convert electricity to and from compressed air stored in submerged accumulators. This work presents three papers that collectively focus on the design and optimization of an UWCAES system. In the first paper, the field performance of a distensible air accumulator is studied for application in UWCAES systems. It is followed by a paper that analyzed the energetic and exergetic performance of a theoretical UWCAES system. The final paper presents a multi-objective UWCAES optimization model utilizing a genetic algorithm to determine optimum system configurations

Low-carbon Energy Transition and Planning for Smart Grids

With the growing concerns of climate change and energy crisis, the energy transition from fossil-based systems to a low-carbon society is an inevitable trend. Power system planning plays an essential role in the energy transition of the power sector to accommodate the integration of renewable energy and meet the goal of decreasing carbon emissions while maintaining the economical, secure, and reliable operations of power systems. In this thesis, a low-carbon energy transition framework and strategies are proposed for the future smart grid, which comprehensively consider the planning and operation of the electricity networks, the emission control strategies with the carbon response of the end-users, and carbon-related trading mechanisms. The planning approach considers the collaborative planning of different types of networks under the smart grid context. Transportation electrification is considered as a key segment in the energy transition of power systems, so the planning of charging infrastructure for electric vehicles (EVs) and hydrogen refueling infrastructure for fuel cell electric vehicles is jointly solved with the electricity network expansion. The vulnerability assessment tools are proposed to evaluate the coupled networks towards extreme events. Based on the carbon footprint tracking technologies, emission control can be realized from both the generation side and the demand side. The operation of the low-carbon oriented power system is modeled in a combined energy and carbon market, which fully considers the carbon emission right trading and renewable energy certificates trading of the market participants. Several benchmark systems have been used to demonstrate the effectiveness of the proposed planning approach. Comparative studies to existing approaches in the literature, where applicable, have also been conducted. The simulation results verify the practical applicability of this method

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

Planning and operating energy storage for maximum technical and financial benefits in electricity distribution networks

PhD ThesisThe transmission and distribution networks are facing changes in the way they will be planned, operated and maintained as a result of the rise in the deployment of Low Carbon Technologies (LCTs) on the power grid. These LCTs provide the benefits of a decarbonised grid and reduce reliance on fossil fuels and large centralised generation. As LCTs are close to the demand centres, a significant amount will be deployed in distribution networks. The distribution networks face challenges in enabling a wide deployment of LCTs because they were traditionally built for centralised generation and most are operated passively as demand patterns are well understood and power flows are unidirectional to load centres. The opposite will be the case for distribution networks with LCTs. Utilities that own and operate distribution networks such as the DNOs in the UK will face a host of problems, such as voltage and thermal excursions and power quality issues on their networks. Traditional reinforcement methods will be expensive for DNOs, so they are considering innovative solutions that provide multiple benefits; this is where Energy Storage Systems (ESS) could play a role to provide multiple technical and economic benefits across the grid from voltage and power flow management to upgrade deferral of network assets. This is due to the multifunctional nature of ESS allowing it to act as generation, transmission, demand or demand response based on requirements at any specific time based on the requirements of the stakeholder involved with the asset. ESS is technically capable of providing benefits to DNOs and other stakeholders on the electricity grid but the business case is not proven. Unless multiple benefits are aggregated, investment in ESS is challenging as they have a substantial capital cost and some components will require more frequent replacement than traditional network assets which typically last between 20 – 40+ years. As a result there is a reluctance to include them in future distribution network planning arrangements. IV Furthermore, the electricity regulatory and market design, which was set up in the time of traditional centralised generation and networks, limits investment in ESS by regulated bodies such as DNOs. The regulations and market structures also affects revenue streams and the resulting business case for ESS. This thesis investigates the feasibility of ESS in distribution networks by first studying the effect of current electricity regulatory and market practices on ESS deployment, investigating how ESS can be used under the present rules, and establishing whether there are limitations that can be reduced or removed. Secondly, short and medium term planning is carried out on model Medium Voltage distribution networks (6.6 kV) provided by the IEEE and Electricity North West Limited to establish the technical and financial viability of investing in ESS over conventional reinforcement methods by: Assessing the impact of the proliferation of LCTs in distribution networks using both deterministic and stochastic methods under different scenarios based on current developments and government policies in the UK. This stochastic evaluation considers both spatial and temporal aspects of LCTs in distribution networks with datasets obtained from real distribution network customers; Developing and applying ESS voltage and power flow management, and market control algorithms to resolve distribution network issues resulting from growing LCTs and allowing ESS to participate in the electricity spot market over a planning period up to the year 2030; Providing a framework for assessing the business case of ESS under a DNO or third-party ownership structure where technical and commercial benefits from network asset upgrade deferral, energy arbitrage, balancing market and ancillary services (frequency response and short term operating reserves), distribution and transmission system use of system benefits are evaluated; V Optimising the operation of ESS considering multiple technical and commercial objectives to establish the technical benefits and revenues that can be obtained from an ESS deployment and the trade-off of benefits that applies for differing ownership types. The simulation results show that, under the scenarios investigated, ESS can be used as a technical solution for DNOs. They show that the ESS capital costs can be offset by aggregating benefits from both technical and commercial applications in distribution networks if regulatory and market changes are made. The conclusions offer a perspective to DNOs and third parties’ considering investing in ESS on the electricity grid as it evolves towards a more active, decarbonised system.Electricity North West Limited and Scottish Power Energy Networks sponsored my stud