Operation and planning of distribution networks with integration of renewable distributed generators considering uncertainties: a review

YesDistributed generators (DGs) are a reliable solution to supply economic and reliable electricity to customers. It is the last stage in delivery of electric power which can be defined as an electric power source connected directly to the distribution network or on the customer site. It is necessary to allocate DGs optimally (size, placement and the type) to obtain commercial, technical, environmental and regulatory advantages of power systems. In this context, a comprehensive literature review of uncertainty modeling methods used for modeling uncertain parameters related to renewable DGs as well as methodologies used for the planning and operation of DGs integration into distribution network.This work was supported in part by the SITARA project funded by the British Council and the Department for Business, Innovation and Skills, UK and in part by the University of Bradford, UK under the CCIP grant 66052/000000

Quantum Computing in Logistics and Supply Chain Management an Overview

The work explores the integration of quantum computing into logistics and supply chain management, emphasising its potential for use in complex optimisation problems. The discussion introduces quantum computing principles, focusing on quantum annealing and gate-based quantum computing, with the Quantum Approximate Optimisation Algorithm and Quantum Annealing as key algorithmic approaches. The paper provides an overview of quantum approaches to routing, logistic network design, fleet maintenance, cargo loading, prediction, and scheduling problems. Notably, most solutions in the literature are hybrid, combining quantum and classical computing. The conclusion highlights the early stage of quantum computing, emphasising its potential impact on logistics and supply chain optimisation. In the final overview, the literature is categorised, identifying quantum annealing dominance and a need for more research in prediction and machine learning is highlighted. The consensus is that quantum computing has great potential but faces current hardware limitations, necessitating further advancements for practical implementation

2QAN: A quantum compiler for 2-local qubit Hamiltonian simulation algorithms

Simulating quantum systems is one of the most important potential applications of quantum computers. The high-level circuit defning the simulation needs to be compiled into one that complies with hardware limitations such as qubit architecture (connectivity) and instruction (gate) set. General-purpose quantum compilers work at the gate level and have little knowledge of the mathematical properties of quantum applications, missing further optimization opportunities. Existing application-specifc compilers only apply advanced optimizations in the scheduling procedure and are restricted to the CNOT or CZ gate set. In this work, we develop a compiler, named 2QAN, to optimize quantum circuits for 2-local qubit Hamiltonian simulation problems, a framework which includes the important quantum approximate optimization algorithm (QAOA). In particular, we exploit the flexibility of permuting different operators in the Hamiltonian (no matter whether they commute) and propose permutation-aware techniques for qubit routing, gate optimization and scheduling to minimize compilation overhead. 2QAN can target different architectures and different instruction sets. Compilation results on four applications (up to 50 qubits) and three quantum computers (namely, Google Sycamore, IBMQ Montreal and Rigetti Aspen) show that 2QAN outperforms state-of-theart general-purpose compilers and application-specifc compilers. Specifcally, 2QAN can reduce the number of inserted SWAP gates by 11.5X, reduce overhead in hardware gate count by 68.5X, and reduce overhead in circuit depth by 21X. Experimental results on the Montreal device demonstrate that benchmarks compiled by 2QAN achieve the highest fdelity

Assessing the mapping of quantum algorithms on superconducting quantum processors

Quantum computing is a promising field regarding computation capabilities for several important tasks. One of the most promising solid-state quantum technology, which is being developed at Qutech (TU Delft), is superconducting qubits. In superconducting quantum processors, qubits are arranged along a 2D grid that only permits information transmission between adjacent qubits. In order to run a quantum algorithm on such a processor a mapping strategy of scheduling-placement-routing is needed.Quantum computers hold the promise for solving efficiently important problems in computational sciences that are intractable nowadays by exploiting quantum phenomena such are superposition and entanglement. Research in quantum computing is mainly driven by the development of quantum devices and quantum algorithms. Quantum algorithms can be described by quantum circuits, which are hardware agnostic -- e.g it is assumed that any arbitrary interaction between qubits is possible. However, real quantum processors have a series of constraints that must be complied to when running a quantum algorithm. Therefore, a mapping process that adapts the quantum circuit to chip's constraints is required. The mapping process will, in general, increase the number of gates and/or the circuit depth. As qubits and gates are error prone, it will result in an increment of the failure rate of computation while running the adapted quantum algorithm in a given quantum device. Most of the current mapping models optimize and are assessed based on two metrics: circuit depth (or latency) and number of (movement) operations added; they should be as minimal as possible. However, these metrics are not giving any information about how the mapping process is affecting the reliability of the algorithm. In other words, can still the algorithm produce `good' results after being mapped? The aim of this thesis is to propose some new mapping metrics that allow to study the impact of the mapping process on the algorithm's reliability. These are, quantum fidelity, probability of success of the algorithm and quantum volume. They could be used not only to assess the quality of the mapping procedure but also as parameters to be optimized by the mapping. To this purpose, different quantum algorithms have been mapped into the superconducting quantum processor, called Surface-17, developed at QuTech

Towards Structuring Smart Grid: Energy Scheduling, Parking Lot Allocation, and Charging Management

Nowadays, the conventional power systems are being restructured and changed into smart grids to improve their reliability and efficiency, which brings about better social, economic, and environmental benefits. To build a smart grid, energy scheduling, energy management, parking lot allocation, and charging management of plug-in electric vehicles (PEVs) are important subjects that must be considered. Accordingly, in this dissertation, three problems in structuring a smart grid are investigated. The first problem investigates energy scheduling of smart homes (SHs) to minimize daily energy consumption cost. The challenges of the problem include modeling the technical and economic constraints of the sources and dealing with the variability and uncertainties concerned with the power of the photovoltaic (PV) panels that make the problem a mixed-integer nonlinear programming (MINLP), dynamic (time-varying), and stochastic optimization problem. In order to handle the variability and uncertainties of power of PV panels, we propose a multi-time scale stochastic model predictive control (MPC). We use multi-time scale approach in the stochastic MPC to simultaneously have vast vision for the optimization time horizon and precise resolution for the problem variables. In addition, a combination of genetic algorithm (GA) and linear programming (GA-LP) is applied as the optimization tool. Further, we propose cooperative distributed energy scheduling to enable SHs to share their energy resources in a distributed way. The simulation results demonstrate remarkable cost saving due to cooperation of SHs with one another and the effectiveness of multi-time scale MPC over single-time scale MPC. Compared to the previous studies, this work is the first study that proposes cooperative distributed energy scheduling for SHs and applies multi-time scale optimization. In the second problem, the price-based energy management of SHs for maximizing the daily profit of GENCO is investigated. The goal of GENCO is to design an optimal energy management scheme (optimal prices of electricity) that will maximize its daily profit based on the demand of active customers (SHs) that try to minimize their daily operation cost. In this study, a scenario-based stochastic approach is applied in the energy scheduling problem of each SH to address the variability and uncertainty issues of PV panels. Also, a combination of genetic algorithm (GA) and linear programming (GA-LP) is applied as the optimization tool for the energy scheduling problem of a SH. Moreover, Lambda-Iteration Economic Dispatch and GA approaches are applied to solve the generation scheduling and unit commitment (UC) problems of the GENCO, respectively. The numerical study shows the potential benefit of energy management for both GENCO and SH. Moreover, it is proven that the GENCO needs to implement the optimal scheme of energy management; otherwise, it will not be effective. Compared to the previous studies, the presented study in this paper is the first study that considers the interaction between a GENCO and SHs through the price-controlled energy management to maximize the daily profit of the GENCO and minimize the operation cost of each SH. In the third problem, traffic and grid-based parking lots allocation and charging management of PEVs is investigated from a DISCO’s and a GENCO’s viewpoints. Herein, the DISCO allocates the parking lots to each electrical feeder to minimize the overall cost of planning problem over the planning time horizon (30 years) and the GENCO manages the charging time of PEVs to maximize its daily profit by deferring the most expensive and pollutant generation units. In both planning and operation problems, the driving patterns of the PEVs’ drivers and their reaction respect to the value of incentive (discount on charging fee) and the average daily distance from the parking lot are modeled. The optimization problems of each DISCO and GENCO are solved applying quantum-inspired simulated annealing (SA) algorithm (QSA algorithm) and genetic algorithm (GA), respectively. We demonstrate that the behavioral model of drivers and their driving patterns can remarkably affect the outcomes of planning and operation problems. We show that optimal allocation of parking lots can minimize every DISCO’s planning cost and increase the GENCO’s daily profit. Compared to the previous works, the presented study in this paper is the first study that investigates the optimal parking lot placement problem (from every DISCO’s view point) and the problem of optimal charging management of PEVs (from a GENCO’s point of view) considering the characteristics of electrical distribution network, driving pattern of PEVs, and the behavior of drivers respect to value of introduced incentive and their daily distance from the suggested parking lots. In our future work, we will develop a more efficient smart grid. Specifically, we will investigate the effects of inaccessibility of SHs to the grid and disconnection of SHs in the first problem, model the reaction of other end users (in addition to SHs) based on the price elasticity of demand and their social welfare in the second problem, and propose methods for energy management of end users (in addition to charging management of PEVs) and model the load of end users in the third problem

Resource Management in Green Wireless Communication Networks

The development of wireless technologies has been stimulated by the ever increasing network capacity and the diversity of users' quality of service (QoS) requirements. It is widely anticipated that next-generation wireless networks should be capable of integrating wireless networks with various network architectures and wireless access technologies to provide diverse high-quality ubiquitous wireless accesses for users. However, the existing wireless network architecture may not be able to satisfy explosive wireless access request. Moreover, with the increasing awareness of environmental protection, significant growth of energy consumption caused by the massive traffic demand consequently raises the carbon emission footprint. The emerging of green energy technologies, e.g., solar panel and wind turbine, has provided a promising methodology to sustain operations and management of next-generation wireless networks by powering wireless network devices with eco-friendly green energy. In this thesis, we propose a sustainable wireless network solution as the prototype of next-generation wireless networks to fulfill various QoS requirements of users with harvested energy from natural environments. The sustainable wireless solution aims at establishing multi-tier heterogeneous green wireless communication networks to integrate different wireless services and utilizing green energy supplies to sustain the network operations and management. The solution consists of three steps, 1) establishing conventional green wireless networks, 2) building multi-tier green wireless networks, and 3) allocating and balancing network resources. In the first step, we focus on cost-effectively establishing single-tier green wireless networks to satisfy users' basic QoS requirements by designing efficient network planning algorithm. We formulate the minimum green macro cell BS deployment problem as an optimization problem, which aims at placing the minimum number of BSs to fulfill the basic QoS requirements by harvested energy. A preference level is defined as the guidance for efficient algorithm design to solve the minimum green macro cell BSs deployment problem. After that, we propose a heuristic algorithm, called two-phase constrained green BS placement (TCGBP) algorithm, based on Voronoi diagram. The TCGBP algorithm jointly considers the rate adaptation and power allocation to solve the formulated optimization problem. The performance is verified by extensive simulations, which demonstrate that the TCGBP algorithm can achieve the optimal solution with significantly reduced time complexity. In the second step, we aim at efficiently constructing multi-tier green heterogeneous networks to fulfill high-end QoS requirements of users by placing green small cell BSs. We formulate the green small cell BS deployment and sub-carrier allocation problem as a mixed-integer non-linear programming (MINLP) problem, which targets at deploying the minimum number of green small cell BSs as relay nodes to further improve network capacities and provide high-quality QoS wireless services with harvested energy under the cost constraint. We propose the sub-carrier and traffic over rate (STR) metric to evaluate the contribution of deployed green small cell BSs in both energy and throughput aspects. Based on the metric, two algorithms are designed, namely joint relay node placement and sub-carrier allocation with top-down/bottom-up (RNP-SA-t/b) algorithms. Extensive simulations demonstrate that the proposed algorithms provide simple yet efficient solutions and offer important guidelines on network planning and resource management in two-tier heterogeneous green wireless networks. In the last step, we intend to allocate limited network resources to guarantee the balance of charging and discharging processes. Different from network planning based on statistical historical data, the design of resource allocation algorithm generally concerns relatively short-term resources management, and thus it is essential to accurately estimate the instantaneous energy charging and discharging rates of green wireless network devices. Specifically, we investigate the energy trading issues in green wireless networks, and try to maximize the profits of all cells by determining the optimal price and quantity in each energy trading transaction. Finally, we apply a two-stage leader-follower Stackelberg game to formulate the energy trading problem. By using back induction to obtain the optimal price and quantity of traded energy, we propose an optimal algorithm, called optimal profits energy trading (OPET) algorithm. Our analysis and simulation results demonstrate the optimality performance of OPET algorithm. We believe that our research results in this dissertation can provide insightful guidance in the design of next-generation wireless communication networks with green energy. The algorithms developed in the dissertation offer practical and efficient solutions to build and optimize multi-tier heterogeneous green wireless communication networks

Distribution Network Operation with High Penetration of Renewable Energy Sources. Joint Active/Reactive Power Procurement: A Market-Based Approach for Operation of Distribution Network

Distributed generators (DGs) are proposed as a possible solution to supply economic and reliable electricity to customers. It is adapted to overcome the challenges that are characterized by centralized generation such as transmission and distribution losses, high cost of fossil fuels and environmental damage. This work presents the basic principles of integrating renewable DGs in low voltage distribution networks and particularly focuses on the operation of DG installations and their impacts on active and reactive power. In this thesis, a novel technique that applies the stochastic approach for the operation of distribution networks with considering active network management (ANM) schemes and demand response (DR) within a joint active and reactive distribution market environment is proposed. The projected model is maximized based on social welfare (SW) using market-based joint active and reactive optimal power flow (OPF). The intermittent behaviour of renewable sources (such as solar irradiance and wind speed) and the load demands are modelled through Scenario-Tree technique. The distributed network frame is recast using mixed-integer linear programming (MILP) that is solved by using the GAMS software and then the obtained results are being analysed and discussed. In addition, the impact of wind and solar power penetration on the active and reactive distribution locational prices (D-LMPs) within the distribution market environment is explored in terms of the maximization of SW considering the uncertainty related to solar irradiance, wind speed and load demands. Finally, a realistic case study (16-bus UK generic medium voltage distribution system) is used to demonstrate the effectiveness of the proposed method. Results show that ANM schemes and DR integration lead to an increase in the social welfare and total dispatched active and reactive power and consequently decrease in active and reactive D-LMPs.Ministry of Higher Education and Scientific Research - IraqThe selected author's publications, the published versions of which were attached at the end of the thesis, have been removed due to copyright

Enhanced Computational Intelligence Algorithm for Coverage Optimization of 6G Non-Terrestrial Networks in 3D Space

The next generation 6G communication network is typically characterized by the full connectivity and coverage of Users Equipment (UEs). This leads to the need for moving beyond the traditional two-dimensional (2D) coverage service to the three-dimensional (3D) full-service one. The 6G 3D architecture leverages different types of non-terrestrial or aerial nodes that can act as mobile Base Stations (BSs) such as Unmanned Aerial Vehicles (UAVs), Low Altitude Platforms (LAPs), High-Altitude Platform Stations (HAPSs), or even Low Earth Orbit (LEO) satellites. Moreover, aided technologies have been added to the 6G architecture to dynamically increase its coverage efficiency such as the Reconfigurable Intelligent Surfaces (RIS). In this paper, an enhanced Computational Intelligence (CI) algorithm is introduced for optimizing the coverage of UAV-BSs with respect to their location from RIS in the 3D space of 6G architecture. The regarded problem is formulated as a constrained 3D coverage optimization problem. In order to increase the convergence of the proposed algorithm, it is hybridized with a crossover operator. For the validation of the proposed method, it is tested on different scenarios with large-scale coordinates and compared with many recent and hybrid CI algorithms, as Slime Mould Algorithm (SMA), Lévy Flight Distribution (LFD), hybrid Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA), the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), and hybrid Grey Wolf Optimizer and Cuckoo Search (GWOCS). The experiment and the statistical analysis show the significant efficiency of the proposed algorithm in achieving complete coverage with a lower number of UAV-BSs and without constraints violation. </p