OPTIMAL DESIGN OF ENERGY COMMUNITIES Multi-objective design of multi-vector energy hubs integrated with electric mobility charging systems and acting as an energy community

The present thesis has the aim to develop a tool based on prescriptive analytics to perform the optimal design of several multi-vector energy hubs, integrated with electric mobility charging infrastructures, jointly acting as a local energy community through a posteriori multi-objective function. In Chapter 1 after having introduced the scope of the study, the justification of its relevance, and the main objectives, a brief summary of the publications of the author and his main activities during the PhD program course is reported. In Chapter 2 the energy transition is introduced, underlining the EU environmental targets by 2030 and the main energy trends which the energy sector is facing. Then the main incentive policies which are used to reach the environmental targets are reported and briefly analysed. The focus is moved on the newly introduced concepts of energy communities and collective self-consumers at the EU and at the State Member level. The preliminary implementation of the EU directives in Italy and Spain are evaluated and commented. Finally, the concept of microgrid and nanogrid is reported, as an actual and real representation of integrated energy systems characterized by multiple energy demands and different technologies. Chapter 3 recalls the concept of traditional design and compare it with optimal design. After a brief introduction on the different analytics techniques (descriptive, predictive, prescriptive) the focus is moved to the MILP (Mixed-Integer Linear Programming) problem as a tool of prescriptive analytics which can be used to perform the optimal design. Finally, a review of the state of the art of optimal design algorithms and case studies are reported and the main contributions of the present work are underlined. Chapter 4 introduces the first step towards this thesis objective. At first a deterministic mathematical model capable of performing the optimal design of a single-vector (electricity) energy hub integrated with EVs (Electric Vehicles) infrastructure is reported and applied to the case of a single-family dwelling. The considered technologies are photovoltaic, electric storage systems and charging infrastructures. Later the complexity of the model is increased, by proposing a stochastic mathematical model capable of performing the optimal design of a single-vector energy hub integrated with EVs infrastructure. The model is applied to the Mensa building of the Savona Campus of the University of Genova. Several objective functions are considered and the results are reported and commented. Chapter 5 increases the complexity of the study by introducing a deterministic mathematical model to perform the optimal design of a multi-vector energy hub. Several energy demands are considered (electricity, space heating and cooling, domestic hot water) and the portfolio of technologies is significantly expanded involving electric and thermal RES (Renewable Energy Sources), micro cogeneration units, trigeneration units, conversion units (reversible heat pumps), electric and thermal storage systems and EVs charging infrastructures. A multi-objective function is implemented. The model is applied to the entirety of the Savona Campus of the University of Genova. Chapter 6 reports the final and complete version of the developed mathematical model. This model is able to perform the optimal design of several multi-vector energy hubs, integrated with EVs charging stations, jointly acting as an energy community. The model is then applied to the Opera Pia Engineering compound of the University of Genova through the analysis of two different cases. At first a purely virtual relationship between several hubs is considered similarly to the Italian implementation of the renewable energy community concept. Later, a physical relationship between hubs is investigated similarly to the Spanish implementation of the renewable energy community configuration. Finally, Chapter 7 reports the conclusions and possible future research activities

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

Transitioning power distribution grid into nanostructured ecosystem : prosumer-centric sovereignty

PhD ThesisGrowing acceptance for in-house Distributed Energy Resource (DER) installations at lowvoltage level have gained much significance in recent years due to electricity market liberalisations and opportunities in reduced energy billings through personalised utilisation management for targeted business model. In consequence, modelling of passive customers’ electric power system are progressively transitioned into Prosumer-based settings where presidency for Transactive Energy (TE) system framework is favoured. It amplifies Prosumers’ commitments into annexing TE values during market participations and optimised energy management to earn larger rebates and incentives from TE programs. However, when dealing with mass Behind-The-Meter DER administrations, Utility foresee managerial challenges when dealing with distribution network analysis, planning, protection, and power quality security based on Prosumers’ flexibility in optimising their energy needs. This dissertation contributes prepositions into modelling Distributed Energy Resources Management System (DERMS) as an aggregator designed for Prosumer-centered cooperation, interoperating TE control and coordination as key parameters to market for both optimised energy trading and ancillary services in a Community setting. However, Prosumers are primarily driven to create a profitable business model when modelling their DERMS aggregator. Greedy-optimisation exploitations are negative concerns when decisions made resulted in detrimental-uncoordinated outcomes on Demand-Side Response (DSR) and capacity market engagements. This calls for policy decision makers to contract safe (i.e. cooperative yet competitive tendency) business models for Prosumers to maximise TE values while enhancing network’s power quality metrics and reliability performances. Firstly, digitalisation and nanostructuring of distribution network is suggested to identify Prosumer as a sole energy citizen while extending bilateral trading between Prosumer-to- Prosumer (PtP) with the involvements of other grid operators−TE system. Modelling of Nanogrid environment for DER integrations and establishment of local area network infrastructure for IoT security (i.e. personal computing solutions and data protection) are committed for communal engagements in a decentralise setting. Secondly, a multi-layered Distributed Control Framework (DCF) is proposed using Microsoft Azure cloud-edge platform that cascades energy actors into respective layers of TE control and coordination. Furthermore, modelling of flexi-edge computing architecture is proposed, comprising of Contract-Oriented Sensor-based Application Platform (COSAP) employing Multi-Agent System (MAS) to enhance data-sharing privacy and contract coalition agreements during PtP engagements. Lastly, the Agents of MAS are programmed with cooperative yet competitive intelligences attributed to Reinforcement Learning (RL) and Neural Networks (NN) algorithms to solve multimodal socio-economical and uncertainty problems that corresponded to Prosumers’ dynamic energy priorities within the TE framework. To verify the DERMS aggregator operations, three business models were proposed (i.e. greedy-profit margin, collegial-peak demand, reserved-standalone) to analyse comparative technical/physical and economic/social dimensions. Results showed that the proposed TE-valued DERMS aggregator provides participation versatility in the electricity market that enables competitive edginess when utilising Behind-The-Meter DERs in view of Prosumer’s asset scheduling, bidding strategy, and corroborative ancillary services. Performance metrics were evaluated on both domestic and industrial NG environments against IEEE Standard 2030.7-2017 & 2030.8-2018 compliances to ensure deployment practicability. Subsequently, proposed in-house protection system for DER installation serves as an add-on monitoring service which can be incorporated into existing Advance Distribution Management System (ADMS) for Distribution Service Operator (DSO) and field engineers use, ADMS aggregator. It provides early fault detections and isolation processes from allowing fault current to propagate upstream causing cascading power quality issues across the feeder line. In addition, ADMS aggregator also serves as islanding indicator that distinguishes Nanogrid’s islanding state from unintentional or intentional operations. Therefore, a Overcurrent Current Relay (OCR) is proposed using Fuzzy Logic (FL) algorithm to detect, profile, and provide decisional isolation processes using specified OCRs. Moreover, the proposed expert knowledge in FL is programmed to detect fault crises despite insufficient fault current level contributed by DER (i.e. solar PV system) which conventional OCR fails to trigger

Energy Management Systems for Optimal Operation of Electrical Micro/Nanogrids

Energy management systems (EMSs) are nowadays considered one of the most relevant technical solutions for enhancing the efficiency, reliability, and economy of smart micro/nanogrids, both in terrestrial and vehicular applications. For this reason, the recent technical literature includes numerous technical contributions on EMSs for residential/commercial/vehicular micro/nanogrids that encompass renewable generators and battery storage systems (BSS) The volume “Energy Management Systems for Optimal Operation of Electrical Micro/Nanogrids”, was released as a Special Issue of the journal Energies, published by MDPI, with the aim of expanding the knowledge on EMSs for the optimal operation of electrical micro/nanogrids by presenting topical and high-quality research papers that address open issues in the identified technical field. The volume is a collection of seven research papers authored by research teams from several countries, where different hot topics are accurately explored. The reader will have the possibility to benefit from original scientific results concerning, in particular, the following key topics: distribution systems; smart home/building; battery energy storage; demand uncertainty; energy forecasting; model predictive control; real-time control, microgrid planning; and electrical vehicles

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

Microgrids: Planning, Protection and Control

This Special Issue will include papers related to the planning, protection, and control of smart grids and microgrids, and their applications in the industry, transportation, water, waste, and urban and residential infrastructures. Authors are encouraged to present their latest research; reviews on topics including methods, approaches, systems, and technology; and interfaces to other domains such as big data, cybersecurity, human–machine, sustainability, and smart cities. The planning side of microgrids might include technology selection, scheduling, interconnected microgrids, and their integration with regional energy infrastructures. The protection side of microgrids might include topics related to protection strategies, risk management, protection technologies, abnormal scenario assessments, equipment and system protection layers, fault diagnosis, validation and verification, and intelligent safety systems. The control side of smart grids and microgrids might include control strategies, intelligent control algorithms and systems, control architectures, technologies, embedded systems, monitoring, and deployment and implementation

Allocation of dump load in islanded microgrid using the mixed-integer distributed ant colony optimization with robust backward\forward sweep load flow

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonReliable planning and operation of droop-controlled islanded microgrids (DCIMGs) is fundamental to expand microgrids (MGs) scalability and maximize renewable energy potential. Employing dump loads (DLs) is a promising solution to absorb excess generation during off-peak hours while keeping voltage and frequency within acceptable limits to meet international standards. Considering wind power and demand forecast uncertainties in DCIMG during off-peak hours, the allocation of DL problem was modelled as two problems, viz., deterministic and stochastic. The former problem was tackled using four highly probable deterministic generation and demand mismatch scenarios, while the latter problem was formulated within scenario based stochastic framework for uncertainty modelling. The mixed-integer distributed ant colony optimization (MIDACO) was introduced as a novel application in microgrids to find the optimal location and size of DL as well as the optimal droop setting for distributed generation (DG). Furthermore, to enhance the convergence of the proposed optimization technique, three robust and derivative free load flow methods were developed as novel extensions of the original backward\forward sweep (BFS) for grid-connected MGs. The three load flow methods are called special BFS, improved special BFS, and general BFS. The first two methods rely on one global voltage variable distributed among all DGs, while the latter has more general approach by adopting local voltage at each generating bus. The deterministic multi-objective optimization problem was formulated to minimize voltage and frequency deviation as well as power losses. Inversely, the stochastic multi-objective problem with uncertainty was formulated to minimize total microgrid cost, maximum voltage error, frequency deviation, and total energy loss. The proposed method was applied to the IEEE 33-, 69-, and 118-test systems as modelled in MATLAB environment and further validated against competitive swarm and evolutionary metaheuristics. Various convergence tests were considered to demonstrate the efficacy of the proposed load flow methods with MIDACO’s non-dominated solution. Likewise, different optimization parameters were utilized to investigate their impact on the solution. Moreover, the advantage of multi-objective optimization against single objective was provided for the deterministic optimization problem, while the effect of load model and droop response were also investigated. The obtained results in chapter 5 and 6 further demonstrate the fundamental role of DL in voltage and frequency regulation while minimizing costs and energy losses associated with DCIMG operation. Accordingly, an improved voltage and frequency profiles for the system after DL inclusion were attained in Figure 6.9 and Figure 6.10, respectively. To demonstrate the competitiveness of DL-based energy management system (EMS) against storage-based EMS, a brief cost benefit analysis considering hot water demand was also provided

Optimal Operation and Maximal Hosting Capacity of High-Renewable Islanded Microgrids

With the advancement of technology, renewable power generators such as solar photovoltaics and wind turbines have become cost-effective and competitive compared to traditional generators. On the other hand, carbon emission issues have been globally focused, promoting development of renewable energy. Meanwhile, microgrids have been widely constructed with increasing installation of distributed generators including microturbines and renewable power generators. Challenges from intermittent and uncertain renewable sources, low operating efficiency as well as system stability in the islanded mode still exist for microgrid operation and renewable hosting capacity assessment. To address these unsolved issues, it is worth developing advanced optimal operation and hosting capacity maximization approaches for high-renewable microgrids, which are presented in this thesis. For microgrid operation, economic efficiency, solution robustness and system stability are major concerns to be addressed. In order to achieve cost-effective operation, firstly a new stochastic optimal power flow (OPF) is proposed for islanded microgrids. A linear network operating model which can be used in the OPF problem is specifically developed, while uncertainties of photovoltaic power and loads are addressed by Monte Carlo simulation. Secondly, an improved OPF method with a new iterative solution algorithm is proposed to enhance the accuracy of network operating model and the computing speed. Besides, an advanced probabilistic modelling method is adapted to present real-time uncertainties in the OPF method. Thirdly, a novel stochastic OPF method with consideration of tie-line switching from the grid-connected to the islanded mode while the main grid in contingency is proposed. Security constraints to guarantee the system stability in the islanded mode are formulated. Moreover, a Benders decomposition based solution algorithm is developed, to efficiently solve the OPF problem with a master problem and a sub-problem which formulate the grid-connected and the islanded modes, respectively. Fourthly, a renewable hosting capacity maximization approach for an islanded microgrid, considering system frequency deviation, is proposed. An advanced sensitivity region based optimization method is proposed to address the uncertainties of wind power and loads, thus obtaining a robust solution. The proposed methods have been successfully demonstrated and compared with existing works. Simulation results have verified their feasibility and effectiveness