Automating decision making to help establish norm-based regulations

Norms have been extensively proposed as coordination mechanisms for both agent and human societies. Nevertheless, choosing the norms to regulate a society is by no means straightforward. The reasons are twofold. First, the norms to choose from may not be independent (i.e, they can be related to each other). Second, different preference criteria may be applied when choosing the norms to enact. This paper advances the state of the art by modeling a series of decision-making problems that regulation authorities confront when choosing the policies to establish. In order to do so, we first identify three different norm relationships -namely, generalisation, exclusivity, and substitutability- and we then consider norm representation power, cost, and associated moral values as alternative preference criteria. Thereafter, we show that the decision-making problems faced by policy makers can be encoded as linear programs, and hence solved with the aid of state-of-the-art solvers

The impact of introducing zonal pricing within GB on investment signals to low carbon generation

This work has investigated the premise that utilising zonal pricing for congestion management within Great Britain (GB) with Scotland as a separate price zone than the rest of GB could disincentivise investment in wind generation within areas of the highest wind resource. Computational modelling has shown consistently less installed wind capacity in Scotland in scenarios representing zonal pricing compared with scenarios representing the current GB system. This suggests that in the long term implementing zonal pricing within GB could negatively impact on the investment of low carbon generation in locations with the best renewable resource, which would be the most cost-effective method of meeting carbon reduction targets under the UK Levy Control Framework;The interaction between investing in low carbon generation within multiple price zones and the subsidy framework including a feed-in tariff with Contracts for Difference (CfDs) is a key focus of this work. Multiple scenarios are developed following a discussion of form that the CfD scheme could take in a two-zone GB. These comprise of a base case scenario representing current electricity trading within GB, a scenario in which the current competitive auction system does not change and CfD strike prices remain GB-wide and a scenario in which locational strike prices are introduced.;Computational modelling has taken the form of a two-node linear solver to introduce and discuss the potential impacts of two price zones in GB on investment in low carbon generation and the Scottish Electricity Dispatch Model (SEDM), an eighteen node investment and dispatch model with greater spatial and temporal complexity and thus a more accurate representation of the GB system. The modelling methodology includes representing a range of objective functions, which has been shown to significantly affect the zonal results. Cases have also been revealed in which the SRMC iteration process did not converge for the two zone solver, highlighting the potential issues involved with modelling a subsidy framework like the CfD mechanism within multiple price zones.This work has investigated the premise that utilising zonal pricing for congestion management within Great Britain (GB) with Scotland as a separate price zone than the rest of GB could disincentivise investment in wind generation within areas of the highest wind resource. Computational modelling has shown consistently less installed wind capacity in Scotland in scenarios representing zonal pricing compared with scenarios representing the current GB system. This suggests that in the long term implementing zonal pricing within GB could negatively impact on the investment of low carbon generation in locations with the best renewable resource, which would be the most cost-effective method of meeting carbon reduction targets under the UK Levy Control Framework;The interaction between investing in low carbon generation within multiple price zones and the subsidy framework including a feed-in tariff with Contracts for Difference (CfDs) is a key focus of this work. Multiple scenarios are developed following a discussion of form that the CfD scheme could take in a two-zone GB. These comprise of a base case scenario representing current electricity trading within GB, a scenario in which the current competitive auction system does not change and CfD strike prices remain GB-wide and a scenario in which locational strike prices are introduced.;Computational modelling has taken the form of a two-node linear solver to introduce and discuss the potential impacts of two price zones in GB on investment in low carbon generation and the Scottish Electricity Dispatch Model (SEDM), an eighteen node investment and dispatch model with greater spatial and temporal complexity and thus a more accurate representation of the GB system. The modelling methodology includes representing a range of objective functions, which has been shown to significantly affect the zonal results. Cases have also been revealed in which the SRMC iteration process did not converge for the two zone solver, highlighting the potential issues involved with modelling a subsidy framework like the CfD mechanism within multiple price zones

Maximising the benefit of distributed wind generation through intertemporal Active Network Management

The role of distribution networks is changing. There is a significant drive, influenced by climate change and security of supply issues, to move electricity generation towards renewable technologies. This is leading to an increase in demand for renewable generation connections at the distribution network level and putting pressure on distribution network operators to change the 'fit-and-forget' philosophy of network operation to include more active approaches. In the UK this is seen through the development of Active Network Management schemes which manage distributed generation in real-time, applying constraints when required to maintain network limits. In parallel, technologies have been developed that are capable of providing intertemporal flexibility, of which two particular examples are energy storage and flexible demand. The objective of the thesis is to answer the questions: How can energy storage and flexible demand be scheduled in a second-generation Active Network Management scheme? And how should they be operated to gain most benefit from distributed wind generation? To answer these questions, the thesis develops and uses tools to study the optimisation of second-generation Active Network Management schemes including intertemporal technologies. The tools developed include a Dynamic Optimal Power Flow algorithm for management of energy storage and flexible demand. The thesis provides the first fully flexible model of energy storage in this context, the first implementation of principles-of-access in an optimal power flow, and the first detailed study of the role of energy storage and flexible demand in managing thermal limits and reducing curtailment of distributed wind generation. The thesis also develops the theory of Dynamic Locational Marginal Pricing based on the economic information contained in an optimal solution to a Dynamic Optimal Power Flow. The thesis shows this to be a useful way of understanding the economic impact of intertemporal flexibility and monetary flows in markets which contain them. The thesis goes on to provide a detailed report of the application of Dynamic Optimal Power Flow and Dynamic Locational Marginal Pricing to an islanded Active Network Management scheme currently in deployment in the UK. This highlights the ability of the tools developed to contribute to future projects. A conclusions of the thesis is that DOPF provides a useful method of scheduling flexible devices such as energy storage and power systems. It takes full account of network constraints and limitations, and as applied in this thesis, the most complete models of the intertemporal effects of energy storage and flexible demand to date. The studies contained in the thesis show that energy storage and flexible demand can increase the benefit of distributed wind generation in Active Network Management by minimising curtailment and transferring generated electricity to periods during which the energy has greatest value in offsetting expensive, fossil fuel based generation. The thesis notes the importance of a useful definition of the 'benefit' of wind generation in terms of global objectives such as minimising emissions rather than interim objectives such as maximising generation from renewables. The thesis discusses the importance of losses in energy storage, and the relationship of storage and network losses with curtailment of wind and the lost opportunity of generating electricity. In terms of losses, the extension of existing economic analysis methods leads to the result that flexibility will only operate between time-steps where the ratio of prices is greater than the round-trip losses of the store. Within this constraint, effective use of energy storage is shown to result from regular charging and discharging. The comparison between energy storage and flexible demand shows that where there are few losses associated with flexibility in demand it is significantly more successful than energy storage at mitigating the effects of variability in wind. The final study of an islanded distribution network with wind curtailment, concludes that energy storage is less effective that flexible demand at reducing wind curtailment, but can provide benefit through management of peak demand. Flexible demand, in the form of flexible domestic electric heating, is shown to have the ability to provide a significant benefit in terms of reduced wind curtailment. This ability is further enhanced for island situations if demand has a frequency-responsive component.The role of distribution networks is changing. There is a significant drive, influenced by climate change and security of supply issues, to move electricity generation towards renewable technologies. This is leading to an increase in demand for renewable generation connections at the distribution network level and putting pressure on distribution network operators to change the 'fit-and-forget' philosophy of network operation to include more active approaches. In the UK this is seen through the development of Active Network Management schemes which manage distributed generation in real-time, applying constraints when required to maintain network limits. In parallel, technologies have been developed that are capable of providing intertemporal flexibility, of which two particular examples are energy storage and flexible demand. The objective of the thesis is to answer the questions: How can energy storage and flexible demand be scheduled in a second-generation Active Network Management scheme? And how should they be operated to gain most benefit from distributed wind generation? To answer these questions, the thesis develops and uses tools to study the optimisation of second-generation Active Network Management schemes including intertemporal technologies. The tools developed include a Dynamic Optimal Power Flow algorithm for management of energy storage and flexible demand. The thesis provides the first fully flexible model of energy storage in this context, the first implementation of principles-of-access in an optimal power flow, and the first detailed study of the role of energy storage and flexible demand in managing thermal limits and reducing curtailment of distributed wind generation. The thesis also develops the theory of Dynamic Locational Marginal Pricing based on the economic information contained in an optimal solution to a Dynamic Optimal Power Flow. The thesis shows this to be a useful way of understanding the economic impact of intertemporal flexibility and monetary flows in markets which contain them. The thesis goes on to provide a detailed report of the application of Dynamic Optimal Power Flow and Dynamic Locational Marginal Pricing to an islanded Active Network Management scheme currently in deployment in the UK. This highlights the ability of the tools developed to contribute to future projects. A conclusions of the thesis is that DOPF provides a useful method of scheduling flexible devices such as energy storage and power systems. It takes full account of network constraints and limitations, and as applied in this thesis, the most complete models of the intertemporal effects of energy storage and flexible demand to date. The studies contained in the thesis show that energy storage and flexible demand can increase the benefit of distributed wind generation in Active Network Management by minimising curtailment and transferring generated electricity to periods during which the energy has greatest value in offsetting expensive, fossil fuel based generation. The thesis notes the importance of a useful definition of the 'benefit' of wind generation in terms of global objectives such as minimising emissions rather than interim objectives such as maximising generation from renewables. The thesis discusses the importance of losses in energy storage, and the relationship of storage and network losses with curtailment of wind and the lost opportunity of generating electricity. In terms of losses, the extension of existing economic analysis methods leads to the result that flexibility will only operate between time-steps where the ratio of prices is greater than the round-trip losses of the store. Within this constraint, effective use of energy storage is shown to result from regular charging and discharging. The comparison between energy storage and flexible demand shows that where there are few losses associated with flexibility in demand it is significantly more successful than energy storage at mitigating the effects of variability in wind. The final study of an islanded distribution network with wind curtailment, concludes that energy storage is less effective that flexible demand at reducing wind curtailment, but can provide benefit through management of peak demand. Flexible demand, in the form of flexible domestic electric heating, is shown to have the ability to provide a significant benefit in terms of reduced wind curtailment. This ability is further enhanced for island situations if demand has a frequency-responsive component

Radio Frequency Identification System Optimisation Models for Lifecycle of a Durable Product

We address the implementation of radio frequency identification (RFID) technology to support and manage durable products over their entire lifecycle with a focus on optimising the RFID tagging system. We develop general models that optimise the placement of RFID tags on an end product and its components, the allocation of the data on tags, and the selection of RFID tags and their configuration. The primary criteria for optimisation are to maximise the value of RFID data, and to minimise the total cost of the RFID tag system. The total cost of RFID tags comprises costs associated with memory capacity, tag type, software acquisition and maintenance, and number of tags used; the total value for a piece of data is determined by the data usage frequency and the fixed and variable value of data per use across the product lifecycle. The tag placement and data allocation model involves making a decision about these two different objectives–cost and value, which we develop in the form of a multi-objective optimisation problem. The tag selection model aids in the proper selection of RFID tags and their configuration for individual components for a given end product. Given a durable product's characteristics along with its bill-of-materials, a system of RFID tags can efficiently be determined for the product and its components, to help manage and support its lifecycle

Integration of cost modelling within the micro-siting design optimisation of tidal turbine arrays

AbstractThe location of individual turbines within a tidal current turbine array – micro-siting – can have a significant impact on the power that the array may extract from the flow. Due to the infancy of the industry and the challenges of exploiting the resource, the economic costs of realising industrial scale tidal current energy projects are significant and should be considered as one of the key drivers of array design. This paper proposes a framework for the automated design of tidal current turbine arrays in which costs over the lifespan of the array may be modelled and considered as part of the design optimisation process. To demonstrate this approach, the cost of sub-sea cabling is incorporated by implementing a cable-routing algorithm alongside an existing gradient-based array optimisation algorithm. Three idealised test scenarios are used to demonstrate the effects of a financial-return optimising design approach as contrasted with a power maximisation approach