Flexible Transmission: A Comprehensive Review of Concepts, Technologies, and Market

As global concerns regarding climate change are increasing worldwide, the transition towards clean energy sources has accelerated. Accounting for a large share of energy consumption, the electricity sector is experiencing a significant shift towards renewable energy sources. To accommodate this rapid shift, the transmission system requires major upgrades. Although enhancing grid capacity through transmission system expansion is always a solution, this solution is very costly and requires a protracted permitting process. The concept of flexible transmission encompasses a broad range of technologies and market tools that enable effective reconfiguration and manipulation of the power grid for leveraged dispatch of renewable energy resources. The proliferation of such technologies allows for enhanced transfer capability over the current transmission network, thus reducing the need for grid expansion projects. This paper comprehensively reviews flexible transmission technologies and their role in achieving a net-zero carbon emission grid vision. Flexible transmission definitions from different viewpoints are discussed, and mathematical measures to quantify grid flexibility are reviewed. An extensive range of technologies enhancing flexibility across the grid is introduced and explored in detail. The environmental impacts of flexible transmission, including renewable energy utilization and carbon emission reduction, are presented. Finally, market models required for creating proper incentives for the deployment of flexible transmission and regulatory barriers and challenges are discussed

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

Advances and Novel Approaches in Discrete Optimization

Discrete optimization is an important area of Applied Mathematics with a broad spectrum of applications in many fields. This book results from a Special Issue in the journal Mathematics entitled ‘Advances and Novel Approaches in Discrete Optimization’. It contains 17 articles covering a broad spectrum of subjects which have been selected from 43 submitted papers after a thorough refereeing process. Among other topics, it includes seven articles dealing with scheduling problems, e.g., online scheduling, batching, dual and inverse scheduling problems, or uncertain scheduling problems. Other subjects are graphs and applications, evacuation planning, the max-cut problem, capacitated lot-sizing, and packing algorithms

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

Modelling of artificial intelligence based demand side management techniques for mitigating energy poverty in smart grids.

Doctoral degree. University of KwaZulu-Natal, Durban.This research work proposes an artificial intelligence (AI) based model for smart grid initiatives (for South Africa and by extension sub-Saharan Africa, (SSA)) and further incorporates energy justice principles. Spanning the social, technical, economic, environmental, policy and overall impact of smart and just electricity grids, this research begins by investigating declining electricity consumption and demand side management (DSM) potential across South Africa. In addition, technical frameworks such as the combined energy management system (CEMS), co-ordinated centralized energy management system (ConCEMS) and biased load manager home energy management system (BLM-HEMS) are modelled. These systems provide for the integration of all aspects of the electricity grid and their optimization in achieving cost reduction for both the utility and consumers as well as improvement in the consumers quality of life (QoL) and reduction of emissions. Policy and economy-wise, this research work further proposes and models an integrated electrification and expansion model (IEEM) for South Africa, and also addresses the issue of rural marginalization due to poor electricity access for off-grid communities. This is done by proposing a hybrid generation scheme (HGS) which is shown to satisfy sufficiently the requirements of the energy justice framework while significantly reducing the energy burden of households and reducing carbon emissions by over 70%