A probabilistic determination of the impact of FiTs on fuel poverty

Policy makers and interests groups often make claims about the benefits of distributed renewable energy technologies. For example, it is suggested that renewables may have an impact on fuel poverty (Walker, G., 2008). This is uncertain and the research outlined describes a method whereby the potential impact on fuel poverty of solar photovoltaic technology (PV), as supported by the UK Feed-in Tariffs, can be tested. It is suggested that PV targeted at lower income groups may have a higher impact on fuel poverty but only approximately 6% of a sample of households were lifted out of fuel poverty.

Community energy storage business case – Final report

Executive Summary This study explores the potential for energy storage to contribute to the delivery of resilient, low carbon and cost effective community-scale energy systems in order to provide insights for a range of stakeholders, including project developers, investors, policy makers and community organisations. The work involves an integra ted analysis of a number of candidate community-scale energy business models comprising both electrical and thermal energy storage, and ~he roles of key stakeholders involved in financing, delivering and operating such projects. It also includes the results of techno-economic modelling carried out for a range of technical platforms comprising embedded energy generation technologies utilised together with electrical and thermal energy storage systems. The insights provided are intended to underpin decision making in policy development, investment planning and project delivery as part of the UK's journey towards a cost-effective low-carbon energy infrastructure. The aims of the work covered in this study were: • To identify stakeholders in the community energy storage sector, and consider stakeholder roles, benefi ts and barriers • To evaluate potential business models, usin,g relevant recent studies as well as stakeholder input • To assess storage and re lated technologies in the near, medium and long terms, and identify candidate energy storage platforms at both device and system levels through a system-of-systems approach • To examine relevant markets for energy storage, and assess potential value streams applicable to community-scale projects • To carry out a financial feasibility and risk analysis study for specific community-scale scenarios The key findings of the work are summarised below

Evaluating the impacts of community renewable energy initiatives

The UK is encouraging the adoption of distributed renewable energy technologies (RETs) in order to achieve carbon reduction targets and deliver on other energy policy objectives such as energy security. Latterly, through the adoption of a Feed-in Tariff (FiT) mechanism, RETs are now diffusing rapidly into local communities. There is therefore an urgent need to understand the rate and patterns of adoption of these technologies, and evaluate their impacts in specific community contexts. A model for this diffusion of FiT-supported RETs into communities is presented together with a framework for measuring the potential impacts of community energy initiatives over a broad range of recent energy policy indicators. The national register of FiT installations has been analysed alongside small-area socio-economic data such as indices of deprivation in order to explore variations in technology diffusion based upon type of RET, community affluence, built environment density, and geographical location. Particular pathways for the diffusion of RETs in UK communities have been discerned and localities identified and a concurrence with emerging literature on community innovations is discussed. It is shown that photovoltaic technologies (PV) are penetrating more affluent communities in a highly dispersed and isolated nature, but some more specific community activity is also evident targeted at more deprived communities

Probabilistic analysis of solar photovoltaic self-consumption using Bayesian Network Models

In order to assess the systemic value and impacts of multiple PV systems in urban areas, detailed analysis of on-site electricity consumption and of solar PV yield at relatively high temporal resolution is required, together with an understanding of the impacts of stochastic variations in consumption and PV generation. In this study, measured and simulated time series data for consumption and PV generation at 5 and 1 minute resolution for a large number of domestic PV systems are analysed, and a statistical evaluation of self-consumption carried out. The results show a significant variability of annual PV self-consumption across the sample population, with typical median annual self-consumption of 31% and inter-quartile range of 22-44%. 10% of the dwellings exceed a self-consumption of 60% with 10% achieving 14% or less. The results have been used to construct a Bayesian Network model capable of probabilistically analysing self-consumption given consumption and PV generation. This model provides a basis for rapid detailed analysis of the techno-economic characteristics and socio-economic impacts of PV in a range of built environment contexts, from single building to district scales

Probabilistic evaluation of UK domestic solar photovoltaic systems: An integrated geographical information system PV estimation tool

It is shown how key predictor parameters for the spatial estimation of PV yield, self -consumption and thereby economic and social indicators can be extracted from a GIS system and introduced into a Bayesian Network model. This model endogenises the uncertainties and incorporates spatial variability inherent in these parameters. Empirical monthly and annual yield measurements obtained from over 600 PV installations have been obtained and compared with estimated yields obtained by two key solar tools used for performance estimation in the UK – these are PVGIS and the UK Government’s Standard Assessment Procedure (SAP) for domestic buildings. Mean bias estimates and root mean square error estimations were obtained for each tool and the results used to construct an uncertainty distribution in PV yield prediction given key input parameters such as system rating, orientation and tilt. This uncertainty was used to furnish a probabilistic graphical model with a prior distribution for PV yield estimation. This was integrated into a Geographical Information (GIS) system furnished with roof and building stock parameters including roof attributes obtained from lidar data. Elements held in a vector layer of the GIS system can be selected and the resultant distributions of input parameters automatically fed to the model to yield a posterior distribution of the PV yield. The model is able to propagate the yield uncertainty to other probabilistic models, including ones which predict the internal rate of return and self -consumption. The latter is in turn predicted by empirical marginal distributions of domestic electricity consumption. Thus with a given posterior distributions of PV yield, new posterior distributions for the internal rate of return, self-consumption and carbon emission savings are automatically calculated. By integration with GIS this novel approach allows the spatial analysis of the uncertainty pertaining to representative risk factors for PV adoption in the UK, and facilitate the estimation by installers, investors, and local authorities in a manner which endogenises uncertainty

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

Evaluating self-consumption for domestic solar PV: simulation using highly resolved generation and demand data for varying occupant archetypes

A detailed study of the on-site consumption of domestic solar PV generated electricity has been undertaken in order to gain an insight in to the relationships between annual consumption, generation and grid injection and to explore the effect of factors such as orientation and occupant behaviour on self-consumption (SC). Both empirical and simulated generation and export time series data for a large number of PV systems were analysed, and the degree to which SC is predicted by absolute generation and consumption and its variability have been quantified. SC is seen to be generally less than 50%, and the results illustrate the value of probabilistic models for predicting the socioeconomic impacts of domestic PV. As such, the results are significant for evaluating both socioeconomic impacts and distribution network loadflow implications

Probabilistic evaluation of solar photovoltaic systems using Bayesian Networks: a discounted cash flow assessment

Solar PV technology (PV) is now a key contributor worldwide in the transition towards low carbon electricity systems. To date, PV commonly receives subsidies in order to accelerate adoption rates by increasing investor returns. However, many aleatory and epistemic uncertainties exist with regards these potential returns. In order to manage these uncertainties, a probabilistic approach using Bayesian networks has been applied to the techno-economic analysis of domestic solar PV. Using the UK as a representative case study, empirical datasets from over 400 domestic PV systems, together with national domestic electricity usage datasets, have been used to generate and calibrate prior probability distributions for PV yield and domestic electricity consumption respectively for typical urban housing stock. Subsequently, conditional dependencies of PV self-use with regards PV generation and household electricity consumption have been simulated via stochastic modelling using high temporal resolution demand and PV generation data. A Bayesian network model is subsequently applied to deliver posterior probability distributions of key parameters as part of a discounted cash flow analysis. The results indicate the sensitivity of investment returns to specific parameters (including PV self-consumption, PV degradation rates and geographical location), and quantify inherent uncertainties when using economic indicators for the promotion of PV adoption. The results’ implications for potential rates of sector-specific adoption are discussed, and implications for policy makers globally are presented with regards energy policy imperatives, as well as fiscal imperatives of meeting investors’ requirements in terms of returns on investment in a post-subsidy context

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

Evaluating The Impacts Of Community Renewable Energy Initiatives

The UK is encouraging the adoption of distributed renewable energy technologies (RETs) in order to achieve carbon reduction targets and deliver on other energy policy objectives such as energy security. Latterly, through the adoption of a Feed-in Tariff (FiT) mechanism, RETs are now diffusing rapidly into local communities. There is therefore an urgent need to understand the rate and patterns of adoption of these technologies, and evaluate their impacts in specific community contexts. A model for this diffusion of FiT-supported RETs into communities is presented together with a framework for measuring the potential impacts of community energy initiatives over a broad range of recent energy policy indicators. The national register of FiT installations has been analysed alongside small-area socio-economic data such as indices of deprivation in order to explore variations in technology diffusion based upon type of RET, community affluence, built environment density, and geographical location. Particular pathways for the diffusion of RETs in UK communities have been discerned and localities identified and a concurrence with emerging literature on community innovations is discussed. It is shown that photovoltaic technologies (PV) are penetrating more affluent communities in a highly dispersed and isolated nature, but some more specific community activity is also evident targeted at more deprived communities