Evaluating the contribution of PV to social, economic and environmental aspects of community renewable energy projects

For the purpose of the sustainability assessment of distributed renewable energy resources it is desirable to better understand the social, economic and environmental impacts (SEE) resulting from their deployment. Often only one, or at most two, of these knowledge domains is considered, partly due to the difficulty of devising an integrated assessment methodology. An approach based on probabilistic graphical models (PGM), has been developed which helps address this problem. Data for several UK urban census areas have been systematically collected and processed in order to furnish a PGM with the probabilistic data required in order to simultaneously make inferences about the SEE impacts of domestic solar PV, deployed to high penetrations. Results show that an integrated probabilistic assessment contributes to transdisciplinary knowledge, providing decision makers with a tool to facilitate deliberative and systematic evidence-based policy making incorporating diverse stakeholder perspectives

Multi-domain analysis of photovoltaic impacts via integrated spatial and probabilistic modelling

Currently, the impacts of wide-scale implementation of photovoltaic (PV) technology are evaluated in terms of such indicators as rated capacity, energy output or return on investment. However, as PV markets mature, consideration of additional impacts (such as electricity transmission and distribution infrastructure or socio-economic factors) is required to evaluate potential costs and benefits of wide-scale PV in relation to specific policy objectives. This study describes a hybrid GIS spatio-temporal modelling approach integrating probabilistic analysis via a Bayesian technique to evaluate multi-scale/multi-domain impacts of PV. First, a wide-area solar resource modelling approach utilising GIS-based dynamic interpolation is presented and the implications for improved impact analysis on electrical networks are discussed. Subsequently, a GIS-based analysis of PV deployment in an area of constrained electricity network capacity is presented, along with an impact analysis of specific policy implementation upon the spatial distribution of increasing PV penetration. Finally, a Bayesian probabilistic graphical model for assessment of socioeconomic impacts of domestic PV at high penetrations is demonstrated. Taken together, the results show that integrated spatio-temporal probabilistic assessment supports multi-domain analysis of the impacts of PV, thereby providing decision makers with a tool to facilitate deliberative and systematic evidence-based policy making incorporating diverse stakeholder perspectives

A new integrated modeling approach to support management decisions of water resources systems under multiple uncertainties

The planning and implementation of effective water resources management strategies need an assessment of multiple (physical, environmental, and socio-economic) issues, and often requires new research in which knowledge of diverse disciplines are combined in a unified methodological and operational framework. Such integrative research to link different knowledge domains faces several practical challenges. The complexities are further compounded by multiple actors frequently with conflicting interests and multiple uncertainties about the consequences of potential management decisions. This thesis aims to overcome some of these challenges, and to demonstrate how new modeling approaches can provide successful integrative water resources research. It focuses on the development of new integrated modeling approaches which allow integration of not only physical processes but also socio-economic and environmental issues and uncertainties inherent in water resources systems. To achieve this goal, two new approaches are developed in this thesis. At first, a Bayesian network (BN)-based decision support tool is developed to conceptualize hydrological and socio-economic interaction for supporting management decisions of coupled groundwater-agricultural systems. The method demonstrates the value of combining different commonly used integrated modeling approaches. Coupled component models are applied to simulate the nonlinearity and feedbacks of strongly interacting groundwater-agricultural hydrosystems. Afterwards, a BN is used to integrate the coupled component model results with empirical knowledge and stakeholder inputs. In the second part of this thesis, a fuzzy-stochastic multiple criteria decision analysis tool is developed to systematically quantify both probabilistic and fuzzy uncertainties associated with complex hydrosystems management. It integrates physical process-based models, fuzzy logic, expert involvement and stochastic simulation within a general framework. Subsequently, the proposed new approaches are applied to a water-scarce coastal arid region water management problem in northern Oman, where saltwater intrusion into a coastal aquifer due to excessive groundwater extraction for irrigated agriculture has affected the aquifer sustainability, endangering associated socio-economic conditions as well as traditional social structures. The results show the effectiveness of the proposed methods. The first method can aid in the impact assessment of alternative management interventions on sustainability of aquifer systems while accounting for economic (agriculture) and societal interests (employment in agricultural sector) in the study area. Results from the second method have provided key decision alternatives which can serve as a platform for negotiation and further exploration. In addition, this approach suits to systematically quantify both probabilistic and fuzzy uncertainties associated with the decision problem. The new approaches can be applied to address the complexities and uncertainties inherent in water resource systems to support management decisions, while serving as a platform for stakeholder participation

Improved Decision Support For Water Treatment Plant Operations Via Stochastic Water Quality, Multi-Objective Optimization, and Visual Analytics

Water treatment decision support systems (DSSs) enhance the quality and consistency of decision making in the production and distribution of drinking water. In this work, we review the state-of-the-art for water treatment DSSs and identify three critical research areas&mdash;stochastic water quality, multi-objective optimization, and visual analytics. We advance these research areas through the development of statistical methods, a novel optimization framework, and an interactive visualization software library. Stochastic water quality methods are useful for generating water quality scenarios to characterize source water uncertainty and analyze how variations in water quality impact treatment decisions. However, traditional time series approaches fail to accurately model these data given the short, multivariate nature of water quality records. Due to these data limitations, we developed a non-parametric approach, known as the modified k-Nearest Neighbor (k-NN) bootstrap resampling, which can generate water quality scenarios that capture the statistical properties of historical records. Using a simulation model, the performance of a treatment plant can be evaluated over an ensemble of water quality scenarios. By coupling this simulation with multi-objective optimization, an algorithm can search for alternative operating policies that perform well across these scenarios for multiple treatment objectives. To better understand the conflict between these objectives&mdash;such as disinfection goals, disinfection byproduct formation, and cost&mdash;and to improve treatment operations, we created a new optimization framework that integrates the modified k-NN described above with simulation-optimization techniques. Visual analytics are used to explore the large, high-dimensional datasets produced by treatment optimizations. These iterative, interactive visualizations facilitate decision making, and among visualization methods, parallel coordinates are well-suited for these datasets. However, current software and open source libraries have limited support for these plots. To improve the implementation of parallel coordinates visualizations and to increase access to visual analytics, we created Parasol&mdash;an open source, interactive visualization library to support the development of web applications for multi-objective decision making.</p

Bayesian Networks for the management of Greenhouse Gas emissions in the British agricultural sector

Recent years have witnessed a rapid rise in the development of deterministic and non-deterministic models to estimate human impacts on the environment. An important failing of these models is the difficulty that most people have understanding the results generated by them, the implications to their way of life and also that of future generations. Within the field, the measurement of greenhouse gas emissions (GHG) is one such result. The research described in this paper evaluates the potential of Bayesian Network (BN) models for the task of managing GHG emissions in the British agricultural sector. Case study farms typifying the British agricultural sector were inputted into both, the BN model and CALM, a Carbon accounting tool used by the Country Land and Business Association (CLA) in the UK for the same purpose. Preliminary results show that the BN model provides a better understanding of how the tasks carried out on a farm impact the environment through the generation of GHG emissions. This understanding is achieved by translating the emissions information into their cost in monetary terms using the Shadow Price of Carbon (SPC), something that is not possible using the CALM tool. In this manner, the farming sector should be more inclined to deploy measures for reducing its impact. At the same time, the output of the analysis can be used to generate a business plan that will not have a negative effect on a farm's capital income

Bayesian Networks for the management of greenhouse gas emissions in the British agricultural sector

Recent years have witnessed a rapid rise in the development of deterministic and non-deterministic models to estimate human impacts on the environment. An important failing of these models is the difficulty that most people have understanding the results generated by them, the implications to their way of life and also that of future generations. Within the field, the measurement of greenhouse gas emissions (GHG) is one such result. The research described in this paper evaluates the potential of Bayesian Network (BN) models for the task of managing GHG emissions in the British agricultural sector. Case study farms typifying the British agricultural sector were inputted into both, the BN model and CALM, a Carbon accounting tool used by the Country Land and Business Association (CLA) in the UK for the same purpose. Preliminary results show that the BN model provides a better understanding of how the tasks carried out on a farm impact the environment through the generation of GHG emissions. This understanding is achieved by translating the emissions information into their cost in monetary terms using the Shadow Price of Carbon (SPC), something that is not possible using the CALM tool. In this manner, the farming sector should be more inclined to deploy measures for reducing its impact. At the same time, the output of the analysis can be used to generate a business plan that will not have a negative effect on a farm's capital income

The development of object oriented Bayesian networks to evaluate the social, economic and environmental impacts of solar PV

Domestic and community low carbon technologies are widely heralded as valuable means for delivering sustainability outcomes in the form of social, economic and environmental (SEE) policy objectives. To accelerate their diffusion they have benefited from a significant number and variety of subsidies worldwide. Considerable aleatory and epistemic uncertainties exist, however, both with regard to their net energy contribution and their SEE impacts. Furthermore the socio-economic contexts themselves exhibit enormous variability, and commensurate uncertainties in their parameterisation. This represents a significant risk for policy makers and technology adopters. This work describes an approach to these problems using Bayesian Network models. These are utilised to integrate extant knowledge from a variety of disciplines to quantify SEE impacts and endogenise uncertainties. A large-scale Object Oriented Bayesian network has been developed to model the specific case of solar photovoltaics (PV) installed on UK domestic roofs. Three specific model components have been developed. The PV component characterises the yield of UK systems, the building energy component characterises the energy consumption of the dwellings and their occupants and a third component characterises the building stock in four English urban communities. Three representative SEE indicators, fuel affordability, carbon emission reduction and discounted cash flow are integrated and used to test the model s ability to yield meaningful outputs in response to varying inputs. The variability in the percentage of the three indicators is highly responsive to the dwellings built form, age and orientation, but is not just due to building and solar physics but also to socio-economic factors. The model can accept observations or evidence in order to create scenarios which facilitate deliberative decision making. The BN methodology contributes to the synthesis of new knowledge from extant knowledge located between disciplines . As well as insights into the impacts of high PV penetration, an epistemic contribution has been made to transdisciplinary building energy modelling which can be replicated with a variety of low carbon interventions

RISK MANAGEMENT SYSTEM TO GUIDE BUILDING CONSTRUCTION PROJECTS’ IN DEVELOPING COUNTRIES: A CASE STUDY OF NIGERIA

A thesis submitted in partial fulfilment of the Requirements of the University of Wolverhampton For the degree of Doctor of PhilosophyProject risk assessment is an effective tool for planning and controlling cost, time and achieving the technical performance of a building construction project. Construction projects often face a lot of uncertainties, which places building construction projects at the risk of cost, time overruns as well as poor quality delivery. Considering the limited resources of developing countries, there is need to complete building projects on-time, on-budget, and to meet optimal quality hence, risk management is an important part of the decision making process in construction industry as it determines the success or failure of construction projects. In line with this need, this research aims to establish a system to improve the time, cost and quality performance of building construction projects in developing countries, through a comprehensive risk management model that ensures the expectations of clients are met. To achieve the aim of this research, a mixed methodological approach was adopted. Through the review of literature, a conceptual risk management framework suitable to elaborate risk assessment of building construction projects especially for developing countries was developed. A questionnaire survey using a nonprobability sampling technique was conducted to elicit information from construction professionals in Nigeria to assess their perception of 79 risk factors identified from literature review based on the likelihood of occurrence and impact on projects using a five point scale. Responses from 343 construction professionals were drawn from 305 contractors and subcontractors and 38 clients (private and public) within the Nigerian construction sector. Response data was subjected to descriptive statistics to depict the frequency distribution and central tendency of responses. Subsequently, the risk acceptability matrix (RAM) was adopted to categorise and prioritise risk factors. 27 critical risks that affect building construction projects were identified. A Bayesian Belief Network (BBN) model was developed by structural learning and used to examine the cause and effect relationship amongst the 27 critical risk factors. The developed BBN model was subjected to validation using a multiple case study of two building construction projects in Nigeria. The result showed the interrelation between the 27 risk factors and how they contributed to cost and time overruns as well as quality problems. The critical risks directly affecting the cost of building construction project were: fluctuation of material prices; health and safety issues; bribery and corruption; material wastage; poor site management and supervision; and time overruns. The critical factors identified to directly affect quality were: supply of defective materials; working under harsh conditions; improper construction methods; lack of protective equipment; ineffective time allocation; poor communication between involved stakeholders; and unsuitable leadership style. Time overruns on building construction projects was directly caused by: quality problems; low productivity; improper construction methods; poor communication between involved parties; delayed payments in contracts; and poor site management and supervision. As a consolidation of the findings of this research, a BBN model for identifying risk factors that directly affect time, cost and quality on building construction projects has been developed which has the potential for assisting construction stake holders to manage risks on their projects. In view of the findings, a best practice system for risk management in building construction projects in Nigeria has been developed with an implementation guide to help building construction practitioners to successfully implement risk management on their building construction projects. Suitable risk responses, also in the form of recommendations have been identified. The strategies include actions to be taken to respond to risks based on their perceived significance or acceptability as well as some positive risk responses, such as exploiting, sharing, enhancing and accepting, and other negative risk responses, such as avoidance, mitigation transfer and acceptanc