Agent-based homeostatic control for green energy in the smart grid

With dwindling non-renewable energy reserves and the adverse effects of climate change, the development of the smart electricity grid is seen as key to solving global energy security issues and to reducing carbon emissions. In this respect, there is a growing need to integrate renewable (or green) energy sources in the grid. However, the intermittency of these energy sources requires that demand must also be made more responsive to changes in supply, and a number of smart grid technologies are being developed, such as high-capacity batteries and smart meters for the home, to enable consumers to be more responsive to conditions on the grid in real-time. Traditional solutions based on these technologies, however, tend to ignore the fact that individual consumers will behave in such a way that best satisfies their own preferences to use or store energy (as opposed to that of the supplier or the grid operator). Hence, in practice, it is unclear how these solutions will cope with large numbers of consumers using their devices in this way. Against this background, in this paper, we develop novel control mechanisms based on the use of autonomous agents to better incorporate consumer preferences in managing demand. These agents, residing on consumers' smart meters, can both communicate with the grid and optimise their owner's energy consumption to satisfy their preferences. More specifically, we provide a novel control mechanism that models and controls a system comprising of a green energy supplier operating within the grid and a number of individual homes (each possibly owning a storage device). This control mechanism is based on the concept of homeostasis whereby control signals are sent to individual components of a system, based on their continuous feedback, in order to change their state so that the system may reach a stable equilibrium. Thus, we define a new carbon-based pricing mechanism for this green energy supplier that takes advantage of carbon-intensity signals available on the internet in order to provide real-time pricing. The pricing scheme is designed in such a way that it can be readily implemented using existing communication technologies and is easily understandable by consumers. Building upon this, we develop new control signals that the supplier can use to incentivise agents to shift demand (using their storage device) to times when green energy is available. Moreover, we show how these signals can be adapted according to changes in supply and to various degrees of penetration of storage in the system. We empirically evaluate our system and show that, when all homes are equipped with storage devices, the supplier can significantly reduce its reliance on other carbon-emitting power sources to cater for its own shortfalls. By so doing, the supplier reduces the carbon emission of the system by up to 25% while the consumer reduces its costs by up to 14.5%. Finally, we demonstrate that our homeostatic control mechanism is not sensitive to small prediction errors and the supplier is incentivised to accurately predict its green production to minimise costs

Chapter 7 - Energy systems

Stabilizing greenhouse gas (GHG) concentrations will require large-scale transformations in human societies, from the way that we produce and consume energy to how we use the land surface. A natural question in this context is what will be the .transformation pathway. towards stabilization; that is, how do we get from here to there? The topic of this chapter is transformation pathways. The chapter is primarily motivated by three questions. First, what are the near-term and future choices that define transformation pathways, including the goal itself, the emissions pathway to the goal, technologies used for and sectors contributing to mitigation, the nature of international coordination, and mitigation policies? Second, what are the key characteristics of different transformation pathways, including the rates of emissions reductions and deployment of low-carbon energy, the magnitude and timing of aggregate economic costs, and the implications for other policy objectives such as those generally associated with sustainable development? Third, how will actions taken today influence the options that might be available in the future? As part of the assessment in this chapter, data from over 1000 new scenarios published since the IPCC Fourth Assessment Report (AR4) were collected from integrated modelling research groups, many from large-scale model intercomparison studies. In comparison to AR4, new scenarios, both in this AR5 dataset and more broadly in the literature assessed in this chapter, consider more ambitious concentration goals, a wider range of assumptions about technology, and more possibilities for delays in additional global mitigation beyond that of today and fragmented international action

Accuracy of energy prediction methodologies

In the current market, the specific annual energy yield (kWh/kWp) of a PV system is gaining in importance due to its direct link to the financial returns for possible investors who typically demand an accuracy of 5% in this prediction. This paper focuses on the energy prediction of photovoltaic modules themselves, as there have been significant advances achieved with module technologies which affect the device physics in a way that might force the revisiting of device modelling. The paper reports the results of a round robin based evaluation of European modelling methodologies. The results indicate that the error in predicting energy yield for the same module at different locations was within 5% for most of the methodologies. However, this error increased significantly if the nominal nameplate rating is used in the characterization stage. For similar modules at the same location the uncertainties were much larger due to module-module variations

Accuracy of Energy Prediction Methodologies

In the current market, the specific annual energy yield (kWh/kWp) of a PV system is gaining in importance due to its direct link to the financial returns for possible investors who typically demand an accuracy of 5% in this prediction. This paper focuses on the energy prediction of photovoltaic modules themselves, as there have been significant advances achieved with module technologies which affect the device physics in a way that might force the revisiting of device modelling. The paper reports the results of a round robin based evaluation of European modelling methodologies. The results indicate that the error in predicting energy yield for the same module at different locations was within 5% for most of the methodologies. However, this error increased significantly if the nominal nameplate rating is used in the characterization stage. For similar modules at the same location the uncertainties were much larger due to module-module variations

Toward conservational anthropology: addressing anthropocentric bias in anthropology

Anthropological literature addressing conservation and development often blames 'conservationists' as being neo-imperialist in their attempts to institute limits to commercial activities by imposing their post-materialist eco-ideology. The author argues that this view of conservationists is ironic in light of the fact that the very notion of 'development' is arguably an imposition of the (Western) elites. The anthropocentric bias in anthropology also permeates constructivist ethnographies of human-animal 'interactions,' which tend to emphasize the socio-cultural complexity and interconnectivity rather than the unequal and often extractive nature of this 'interaction.' Anthropocentrism is argued to be counteractive to reconciling conservationists' efforts at environmental protection with the traditional ontologies of the interdependency of human-nature relationship

Mapping hail meteorological observations for prediction of erosion in wind turbines

Wind turbines are subject to a wide range of environmental conditions during a lifespan that can conceivably extend beyond 20 years. Hailstone impact is thought to be a key factor in the leading edge erosion and damage of wind turbine blades. Along with the size and density of the hailstone, the aggregated impact velocity components are crucial variables that characterise the kinetic energy associated with singular impact. These components include: the terminal velocity of the hailstone, the mean wind speed and the rotational speed of the turbine. Theorised values for the impact velocity may not truly reflect the conditions experienced by wind turbine blades. Using UK meteorological data, a greater representation of hail characteristics, occurrence probabilities and realistic impact component velocities is proposed, which will assist in the development of a realistic damage model for hailstone impact