Energy Management Systems and Potential Applications of Quantum Computing in the Energy Sector

The combined use of technologies plays a key role in the energy transition towards a green and sustainable economy, driven by the European Green Deal initiatives and the Paris Agreement to achieve climate neutrality in the European Union (EU) by 2050. Indeed, all viable solutions with no barriers to innovation should be considered if a fair, cost-effective, competitive, and green transition is to be ensured.Energy hubs enable the synergy of different forms of energy by exploiting their specific vir-tues. However, their management in an integrated context must be entrusted to automated manage-ment systems capable of making real-time decisions.This PhD thesis aims to assess the main potential applications of quantum computing to the energy sector in the current development scenario of quantum technologies, as well as provide the elements for modelling an energy hub and managing uncertainties.The thesis is organized as follows. Chapter 1 provides an introduction to energy manage-ment systems. The concept of an energy hub and its mathematical modelling are introduced in chap-ter 2. Chapter 3 introduces the fundamentals of energy supply. Chapter 4 examines potential use cases for quantum computing in the energy sector. Chapter 5 addresses the modelling of uncertain parameters. Chapter 6 concludes the thesis with a case study of two urban districts modelled as mul-ticarrier energy hubs connected by a multicarrier energy infrastructure providing electricity, gas and hydrogen. The conclusions are drawn in chapter 7. The appendices with additional insights enrich the thesis, which is full of comments and bibliographical references

Advanced Control and Optimization for Future Grid with Energy Storage Devices

In the future grid environment, more sustainable resources will be increasing steadily. Their inherent unpredictable and intermittent characteristics will inevitably cause adverse impacts on the system static, dynamic and economic performance simultaneously. In this context, energy storage (ES) devices have been receiving growing attention because of their significant falling prices. Therefore, how to utilize these ES to help alleviate the problem of renewable energy (RE) sources integration has become more and more attractive. In my thesis, I will try to resolve some of the related problems from several perspectives. First of all, a comprehensive Future Australian transmission network simulation platform is constructed in the software DIgSILENT. Then in-depth research has been done on the aspect of frequency controller design. Based on mathematical reasoning, an advanced robust H∞ Load Frequency Controller (LFC) is developed, which can be used to assist the power system to maintain a stable frequency when accommodating more renewables. Afterwards, I develop a power system sensitivity analysis based-Enhanced Optimal Distributed Consensus Algorithm (EODCA). In the following study, a Modified Consensus Alternating Direction Method of Multipliers (MC-ADMM) is proposed, with this approach it can be verified that the convergence speed is notably accelerated even for complex large dimensional systems. Overall, in the Master thesis, I successfully provide several novel and practical solutions, algorithms and methodologies in regards to tackling both the frequency, voltage and the power flow issues in a future grid with the assistance of energy storage devices. The scientific control and optimal dispatch of these facilities could provide us with a promising approach to mitigate the potential threats that the intermittent renewables posed on the power system in the following decades

Application of distributed optimization technique for large-scale optimal power flow problem

Optimal Power Flow (OPF) is one the most basic problems in power system analysis. In the last decades many studies have been done to provide a robust and fast solution for OPF. The main goal of the Optimal Power Flow problem is determining an operating point that minimizes the power system objectives such as generation cost, emission or power loss. The conventional optimization algorithms for Optimal Power Flow are centralized algorithms. These conventional centralized algorithms encounter two challenges. First, most times the generation units in a power network belong to different owners that do not want to share their confidential information with other power generation companies. Second, when the number of buses significantly increase the optimization problem will be very complicated. In these cases, finding the optimal solution takes time and in some cases even the solution does not converge. In order to solve the large-scale Optimal Power Flow problem in power networks and deal with the problems brought by the system size, distributed parallel processing algorithms, which are known distributed optimization techniques, are sought. In distributed parallel algorithms, each processor tries to solve a sub-problem independently based on limited information communication. This research discusses a consensus-based Alternating Direction Method of Multipliers (ADMM) approach for solving the OPF problem. In distributed optimization, the whole power system should be split into some partitions. Sub-problems which are related to partitions should be solved by their assigned local processors in parallel. The local processors have to be networked. In the proposed distributed optimization technique, the optimal point of the whole system can be obtained throughout the ADMM iterative process. In this thesis, ADMM implementation on an OPF problem for some IEEE cases has been presented and the optimal solution obtained by ADMM and MATPOWER (a MATLAB base program) are compared

Economic Operation of Virtual Power Plants with Electric Vehicle Charging Stations

Energy management of distributed energy resources (DERs) is challenging due to the distributed and uncertain nature of DERs. To optimally operate DERs and trade their energy as well as energy flexibility for financial benefits, energy management for virtual power plants (VPPs) and electric vehicle (EV) charging stations are investigated in this thesis. The research in this thesis can be summarized into three parts. Part I provides a VPP operation strategy in the electricity market environment. Part II proposes an EV charging station operation strategy considering EV user incentives. Part III develops a coordinated VPP and EV charging station operation framework based on the methods proposed in parts I and II. (1) Economic VPP operation In this part, an optimal VPP operation regime is proposed considering multiple electricity markets and multiple uncertainties. The proposed operation regime handles both the VPP market bidding and unit dispatching problems. To deal with uncertainties, a hybrid stochastic minimax regret optimization model is proposed. To reduce the conservativeness of the formulated optimization models, a self-adaptive algorithm is proposed. (2) Economic EV charging station operation In this part, an EV charging station operation strategy with an EV user incentive program is proposed to improve the EV charging station economic benefit. To maximize the long-term profit of the EV charging station, an optimal incentive price selection model is developed. In the solution methodology, a problem linearization method is first proposed. Then, a distributed solution methodology is developed based on the proposed adaptive alternating-direction-methodof-multipliers algorithm. (3) Economic VPP operation considering EV charging stations i In this part, a multi-stakeholder VPP-charging station system is investigated. Firstly, a coordinated operation framework is proposed for the VPP-charging station system to maximize the total benefit of the system. Then, an improved EV user incentive program is proposed for acquiring EV energy flexibility. At the cost allocation stage, a τ -value cost allocation method is developed. To alleviate the computation burden in calculating the τ -values, a τ -values estimation approach is proposed. The effectiveness of the energy management methods proposed in this thesis is verified through theoretical analysis and numerical simulations. Significant results suggest high potential for practical application in certain scenarios

Service Revenue Evaluation Methodologies to Maximize the Benefits of Energy Storage

The objective of this research is to develop novel methodologies and tools for service revenue evaluation of electrical energy storage systems. Energy storage systems can provide a wide range of services and benefits to the entire value chain of the electricity industry and, therefore, are becoming a favorable technology among stakeholders. The U.S. Government and various states have set initiatives and mandated energy storage deployment as part of their grid modernization roadmap. The key to an increased deployment of energy storage projects is their economic viability. Because of the significant potential value of energy storage as well as the complexity of the decision-making problem, sophisticated service evaluation methodologies and service optimization tools are highly needed. The maximum potential value of energy storage cannot be captured with the evaluation methodologies that have been developed for conventional generators or other distributed energy resources. Previous research studies mostly operational strategies for energy storage coupled with renewable energy sources and the benefits and business models of privately-owned energy storage systems are not well understood. Most of the existing literature focuses on evaluating energy storage systems providing a single service while multiservice operation and evaluation is often not considered. The few available methods for multiservice evaluation study a limited number of services and cannot be readily implemented into a computational tool due to complexity and scalability issues. Accordingly, this research proposes novel service evaluation methodologies with two main objectives: a. Discover the maximum value of energy storage systems for single and multiservice applications, b. Provide flexibility, scalability and tractability of implementation. In order to meet these objectives, various methodologies based on statistical analysis, dynamic control, mixed integer linear programming, convex optimization and decomposition have been proposed. The challenges, complexities, and the benefits of modeling energy services using a scalable approach are analyzed, solutions are proposed and simulated with realistic data in three main chapters of this research: a) energy storage in wholesale energy markets, b) generic multiservice revenue analysis of energy storage, and c) temporal complexities of energy storage optimization models: value and decomposition. Simulation results show the feasibility of the proposed approaches, and significant added values to the economic viability of energy storage projects using the proposed methodologies. Energy storage decision makers including public utility commissioners, transmission/distribution system operators, aggregators, private energy storage owners/investors, and end-use customers (residential and commercial loads) can benefit from the proposed methodologies and simulation results. A software tool has been developed for multiservice benefit cost analysis of energy storage projects. It is hoped that with the significant unlocked value of energy storage systems using the proposed tools and methodologies, more of these technologies be deployed in the future grids to help communities with their sustainability and environmental goals.Ph.D

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

Distributed Control Methods for Integrating Renewable Generations and ICT Systems

With increased energy demand and decreased fossil fuels usages, the penetration of distributed generators (DGs) attracts more and more attention. Currently centralized control approaches can no longer meet real-time requirements for future power system. A proper decentralized control strategy needs to be proposed in order to enhance system voltage stability, reduce system power loss and increase operational security. This thesis has three key contributions: Firstly, a decentralized coordinated reactive power control strategy is proposed to tackle voltage fluctuation issues due to the uncertainty of output of DG. Case study shows results of coordinated control methods which can regulate the voltage level effectively whilst also enlarging the total reactive power capability to reduce the possibility of active power curtailment. Subsequently, the communication system time-delay is considered when analyzing the impact of voltage regulation. Secondly, a consensus distributed alternating direction multiplier method (ADMM) algorithm is improved to solve the optimal power ow (OPF) problem. Both synchronous and asynchronous algorithms are proposed to study the performance of convergence rate. Four different strategies are proposed to mitigate the impact of time-delay. Simulation results show that the optimization of reactive power allocation can minimize system power loss effectively and the proposed weighted autoregressive (AR) strategies can achieve an effective convergence result. Thirdly, a neighboring monitoring scheme based on the reputation rating is proposed to detect and mitigate the potential false data injection attack. The simulation results show that the predictive value can effectively replace the manipulated data. The convergence results based on the predictive value can be very close to the results of normal case without cyber attack

Distributionally Robust and Structure Exploiting Algorithms for Power System Optimization Problems

The modern power systems are undergoing profound changes as the large-scale integration of renewable energy and increasingly close interconnection of regional power grids. The intermittent renewable sources are bringing significant uncertainties to system operation so that all the analysis and optimization tools for the power system steady-state operation must be able to consider and manage the uncertainties. The large-scale interconnection of power systems increases the difficulty in maintaining the synchronization of all generators and further raises the challenging problem of systematically design multiple local and wide-area controllers. In both steady-state and dynamical problems, the large-scale interconnection is increasing the problem scale and challenging the scalability of analysis, optimization and design algorithms. This thesis addresses the problems of power system operation optimization under uncertainties and control parameter optimization considering time delays. The contributions are as follows. This thesis proposes data-driven distributionally robust models and algorithms for unit commitment, energy-reserve-storage co-dispatch and optimal power flow problems based on novel ambiguity sets. The problem formulations minimize the expected operation costs corresponding to the worst-case distribution in the proposed ambiguity set while explicitly considers spinning reserve, wind curtailment, and load shedding. Distributionally robust chance constraints are employed to guarantee reserve adequacy and system steady-state security. The construction of ambiguity set is data-driven avoiding presumptions on the probability distributions of the uncertainties. The specific structures of the problem formulation are fully exploited to develop a scalable and efficient solution method. To improve the efficiency of the algorithms to solve the operation and control optimization problems, this thesis investigates computational techniques to exploit special problem structures, including sparsity, chordal sparsity, group symmetry and parallelizability. By doing so, this thesis proposes a sparsity-constrained OPF framework to solve the FACTS devices allocation problems, introduces a sparsity-exploiting moment-SOS approach to interval power flow (IPF) and multi-period optimal power flow (MOPF) problems, and develops a structure-exploiting delay-dependent stability analysis (DDSA) method for load frequency control (LFC). The power system stabilizers (PSS) and FACTS controllers can be employed improve system damping. However, when time delays are considered, it becomes more difficult to analyzing the stability and designing the controllers. This thesis further develops time-domain methods for analysis and synthesis of damping control systems involving time delays. We propose a model reduction procedure together with a condition to ensure the $\epsilon$-exponential stability of the full-order system only using the reduced close-loop system model, which provides a theoretical guarantee for using model reduction approaches. Then we formulate the damping control design as a nonlinear SDP minimizing a carefully defined $H_2$ performance metric. A path-following method is proposed to coordinately design multiple damping controllers