Simulated Annealing Heuristics for Managing Resources during Planned Outages at Electric Power Plants

This paper presents a mathematical model and simulated annealing heuristics for assigning activities to workspaces and resources (e.g., equipment, parts, and toolboxes) to work/storage spaces during planned outages at electric power plants. These assignments are made such that the distance resources (toolboxes) travel throughout the duration of the outage is minimized. This problem is de2ned as the dynamic space allocation problem. To test the performance of the proposed techniques, a data set is generated and used in the analysis. The results show that the simulated annealing heuristics perform well with respect to solution quality and computational time

Solution Techniques for a Crane Sequencing Problem

In the areas of power plant maintenance, shipyard and warehouse management, resources (items) assigned to locations need to be relocated. It is essential to develop efficient techniques for relocating items to new locations using a crane such that the sum of the cost of moving the items and the cost of loading/unloading the items is minimised. This problem is defined as the crane sequencing problem (CSP). Since the CSP determines the routes for a crane to relocate items, it is closely related to some variants of the travelling salesman problem. However, the CSP considers the capacities of locations and intermediate drops (i.e. preemptions) during a multiple period planning horizon. In this article, a mathematical model and hybrid ant systems are developed for the CSP. Computational experiments were conducted to evaluate the performances of the proposed techniques, and results show that the proposed heuristics are effective

Tabu Search Heuristics for the Crane Sequencing Problem

Determining the sequence of relocating items (or resources) moved by a crane from existing positions to newly assigned locations during a multiperiod planning horizon is a complex combinatorial optimisation problem, which exists in power plants, shipyards, and warehouses. Therefore, it is essential to develop a good crane route technique to ensure efficient utilisation of the crane as well as to minimize the cost of operating the crane. This problem was defined as the Crane Sequencing Problem (CSP). In this paper, three construction and three improvement algorithms are presented for the CSP. The first improvement heuristic is a simple Tabu Search (TS) heuristic. The second is a probabilistic TS heuristic, and the third adds diversification and intensification strategies to the first. The computational experiments show that the proposed TS heuristics produce high-quality solutions in reasonable computation time

Applications of Operations Research in Domestic Electric Utilities

Since its inception in the 1950s, operations research has been used in a number of industries, including the energy industry. Documentation of its use in exploration, production, gasoline blending, oil spill management, coal mining, coal handling, and coal mixing is extensive. However, considerably less documented research exists for one significant customer of many of these products: the electric utility. This work reviews refereed literature from United States operations research journals that document the use of operations research in United States electric utility operations. Applications that centered specifically on the areas of thermal energy generation, transmission, distribution, capacity planning, electric power service options, and other general operations-related activities were included. Applications solely related to plant siting, general energy policy, or work that focused on electricity as a commodity and primarily investigated the use of financial instruments, were not included

Agent-based technology applied to power systems reliability

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200

Solution techniques for a crane sequencing problem

In shipyards and power plants, relocating resources (items) from existing positions to newly assigned locations are costly and may represent a significant portion of the overall project budget. Since the crane is the most popular material handling equipment for relocating bulky items, it is essential to develop a good crane route to ensure efficient utilization and lower cost. In this research, minimizing the total travel and loading/unloading costs for the crane to relocate resources in multiple time periods is defined as the crane sequencing problem (CSP). In other words, the objective of the CSP is to find routes such that the cost of crane travel and resource loading/unloading is minimized. However, the CSP considers the capacities of locations and intermediate drops (i.e., preemptions) during a multiple period planning horizon. Therefore, the CSP is a unique problem with many applications and is computationally intractable. A mathematical model is developed to obtain optimal solutions for small size problems. Since large size CSPs are computationally intractable, construction algorithms as well as improvement heuristics (e.g., simulated annealing, hybrid ant systems and tabu search heuristics) are proposed to solve the CSPs. Two sets of test problems with different problem sizes are generated to test the proposed heuristics. In other words, extensive computational experiments are conducted to evaluate the performances of the proposed heuristics

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

Operational Planning and Optimisation in Active Distribution Systems for Flexible and Resilient Power

The electricity network is undergoing significant changes to cater to environmental-deterioration and fuel-depletion issues. Consequently, an increasing number of renewable resources in the form of distributed generation (DG) are being integrated into medium-voltage distribution networks. The DG integration has created several technical and economic challenges for distribution network operators. The main challenge is basically the problem of managing network voltage profile and congestion which is caused by increasing demand and intermittent DG operations. The result of all of these changes is a paradigm shift in the way distribution networks operate (from passive to active) and are managed that is not limited only to the distribution network operator but actively engages with network users such as demand aggregators, DG owners, and transmission-system operators. This thesis expands knowledge on the active distribution system in three specific areas and attempts to fill the gaps in existing approaches. A comprehensive active network management framework in active distribution systems is developed to allow studies on (i) the flexibility of network topology using modern power flow controllers, (ii) the benefits of centralised thermal electricity storage in achieving the required levels of flexibility and resiliency in an active distribution system, and (iii) system resiliency toward fault occurrence in hybrid AC/DC distribution systems. These works are implemented within the Advanced Interactive Multidimensional Modelling Systems (AIMMS) software to carry out optimisation procedure. Results demonstrate the benefit provided by a range of active distribution system solutions and can guide future distribution-system operators in making practical decisions to operate active distribution systems in cost-effective ways

Adding multi-objective optimisation capability to an electricity utility energy flow simulator

Thesis (MEng)--Stellenbosch University, 2016.ENGLISH ABSTRACT: The energy flow simulator (EFS) is a strategic decision support tool that was developed for the South African national electricity utility Eskom. The advanced set of algorithms incorporated into the EFS enables various departments within Eskom to simulate and analyse the Eskom value chain from primary energy to end-use over a certain study horizon. The research in this thesis is aimed at determining whether multi-objective optimisation (MOO) capability can be added to the EFS. The study forms part of a series of research projects. This project builds on the work of Hatton (2015) in which the focus was on single-objective optimisation capability for the EFS. Inventory management at Eskom's coal- red power stations was identified as the most suitable area for the formulation of an MOO model. It was also identified that certain modifications to the existing EFS architecture can possibly improve its potential as an optimisation tool. The architecture of the EFS is studied and modifications to it are proposed. A multi-objective inventory model is then formulated for Eskom's network of coal- red power stations using the simulation outputs of the EFS. The model is based on the movement of coal between the various power stations in an attempt to maintain an optimal inventory level at each station as far as possible. To solve the model, a suitable MOO algorithm is selected and integrated with the simulation component of the EFS. Several experiments are conducted to validate the MOO model and test the e effectiveness of the algorithm in solving the optimisation problem.AFRIKAANSE ABSTRACT: Die energievloei-simulator (EVS) is 'n strategiese besluitondersteuningsinstrument wat ontwikkel is vir die Suid-Afrikaanse nasionale elektrisiteitsverskaffer, Eskom. Die gevorderde stel algoritmes waaruit die EVS bestaan stel verskeie departemente binne Eskom in staat om die Eskom-waardeketting te simuleer en te analiseer, vanaf prim^ere energie tot eindgebruik, oor 'n sekere studie tydperk. Die navorsing in hierdie tesis is daarop gemik om te bepaal of meerdoelige optimeringsvermo e tot die EVS bygevoeg kan word. Die studie vorm deel van 'n reeks navorsingsprojekte. Hierdie projek bou voort op die werk van Hatton (2015) waarin die fokus op enkeldoel-optimeringsvermo e vir die EVS was. Voorraadbestuur by Eskom se steenkoolaangedrewe kragstasies is geidentifiseer as die mees geskikte gebied vir die formulering van 'n meerdoelige optimeringsmodel. Daar is ook geidentifiseer dat sekere veranderinge aan die bestaande argitektuur van die EVS moontlik die model se potensiaal as 'n optimeringsinstrument kan verbeter. Die argitektuur van die EVS word bestudeer en veranderinge daaraan word voorgestel. 'n Meerdoelige voorraadbestuursmodel word daarna vir Eskom se netwerk van steenkoolaangedrewe kragstasies geformuleer deur die simulasie-uitsette van die EVS te gebruik. Die model is gebaseer op die beweging van steenkool tussen die verskillende kragstasies om 'n optimale voorraadvlak by elke stasie te probeer handhaaf. Om die model op te los word 'n geskikte meerdoelige optimeringsalgoritme gekies en met die EVS se simulasie komponent geintegreer. Verskeie eksperimente word uitgevoer om te bevestig dat die meerdoelige optimeringsmodel korrek is en om die doeltreffendheid van die algoritme as oplossingsmetode vir die probleem te toets

Fault Location, Isolation and Network Restoration as a Self-Healing function

One of the main emphasis of the smart grid is the interaction of power supply and power customer in order to provide a reliable supply of power as well as to improve the flexibility of the network. Along with this, the increased energy demand, coupled with strict regulations on the quality and reliability of supply intensifies the pressure on distribution network operators to maintain the integrity of the network in its faultless operation mode. Additionally, regardless of the huge investments already made in replacing aging infrastructure and translating “the old-fashioned grid” in a “Smart Grid” to minimize the probability for equipment failure, the chances of failure cannot be completely eliminated. In accordance, in the event of faults in the network, apart from the high penalty costs in which network operators may incur, certain safety factors must be taken into consideration for particular customers (for example, hospitals). In view of that, there is a necessity to minimize the impact on customers without supply and maintain outages times as brief as possible. Within this scenario comes the concept of self-healing grid as one of the key-technologies in the smart grid environment which is partly due to the rapid development of distribution automation. Self-healing refers to the capacity of the smart grid to restore efficiently and automatically power after an outage. Self-healing main goals comprise supply maximum load affected by the fault, take the shortest time period possible for restoration of the load, minimizing the number of switching operations and keeping the network capacity within its operating limits. This research has explored insights into the smart grid in terms of the self-healing functionality within the distribution network with main emphasis on self-healing implementation types and its applicability. Initially a detailed review of the conception of the smart grid in order to integrate the self-healing and thus fault location, isolation and service restoration capabilities was conducted. This was complemented with a detailed discussion about the electricity distribution system automatic fault management in order to create a framework around which the aim of the research is based. Finally the self-healing problem coupled with current practical implementation cases was addressed with the objective of exploring the means of improvement and evolution in the automation level in the distribution network using Fault Location Isolation and Service Restoration (FLISR) applicability as a medium