Seeing double: the low-carb diet

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Energy autonomy in residential buildings: a techno-economic modelbased analysis of the scale effects

An increasingly decentralized energy supply structure alongside economic incentives for increasing the level of self-generation and –consumption are encouraging (higher levels of) energy autonomy. Previous work in this area has focused on the technical and economic aspects of energy autonomy at distinct scales, from individual buildings, through neighbourhoods to districts. This paper employs a mixed integer linear program (MILP) to assess the effects of aggregation across these scales on the economics of energy autonomy in residential buildings. The model minimizes total energy system costs over the lifetime of the energy system, including micro-CHP, PV, thermal and electrical storage, and boilers, at five distinct scales and for nine demand cases. It is subject to several constraints, amongst other things the degree of electrical self-sufficiency. The results indicate a shift in the economically optimal level of electrical self-sufficiency with scale, which in Single Family Households (SFHs) means from around 30% at the individual building level to almost 100% in districts of 1000 SFH households. Above around 560 households it could be economically advantageous to make a district of residential buildings electrically self-sufficient. In addition, a marginal increase in electrical selfsufficiency is significantly more expensive at lower aggregation scales (i.e. single buildings) compared to the scale of neighbourhoods and districts. The level of interaction with the electrical distribution network increases with increasing electrical self-sufficiency before then decreasing at very high (above 70%) levels. Future work should focus on a richer socioeconomic differentiation, considering other sectors and technologies, incorporating demand side options and analysing the effects on the overarching energy system

Development of a multi-energy residential service demand model for evaluation of prosumers’ effects on current and future residential load profiles for heat and electricity

The motivation of this thesis is to develop a multi-energy residential service demand (MESD) model. The approach is based on earlier modelling concepts. Electricity is simu- lated by the help of a first-order Markov-chain approach simulating pseudorandom solar irradiation data as well as occupancy patterns, which are matched to stochastically deter- mined electric appliance activities (McKenna et al., 2015; Richardson & Thomson, 2012). A lumped-parameter model simulating indoor temperatures is utilized to estimate space heating (SH) demand (Nielsen, 2005). Measurement data on domestic hot water (DHW) consumption in dwellings is analysed in order to implement a DHW model. The model generates output in 1-minute resolution. It features various possibilities of dwelling customization: Among others, number of residents, building physics, electric appliances and heating regime may be adjusted. An interface providing a link to the Cambridge Housing Model (DECC, 2012) is implemented, which supports automated re- trieval of relevant building parameters. Electricity and DHW demand values may also be extracted to be used for model calibration. The added value of this work is the implementation of a DHW model and the combination of above named approaches to an integrated multi-energy service demand model. The electricity model is enhanced by improving the calibration mechanism and increasing electric appliance variety. The SH model is extended by random heating regime genera- tion based on field data. The model features full year simulations incorporating seasonal effects on DHW and SH demand. In addition, seven representative archetypes have been developed, which allow for detailed investigation of load profiles for heat and electricity of representative UK dwellings. The model has a wide scope of application. It can be used to explore the impact of differ- ent dwelling configurations on load matching and grid interaction throughout the seasons. Synthetic energy service demand profiles may support research on the optimal configura- tion of on-site supply appliances such as mCHP, PV and heat pumps. Furthermore, the model allows for drawing conclusions on the net carbon emissions of a dwelling and for assessing energy-efficiency measures

Comparing empirical and model-based approaches for calculating dynamic grid emission factors: An application to CO2_{2}-minimizing storage dispatch in Germany

As one possibility to increase flexibility, battery storage systems (BSS) will play a key role in the decarbonization of the energy system. The emissions-intensity of grid electricity becomes more important as these BSSs are more widely employed. In this paper, we introduce a novel data basis for the determination of the energy system’s CO$_{2}$ emissions, which is a match between the ENTSO-E database and the EUTL databases. We further postulate four different dynamic emission factors (EF) to determine the hourly CO$_{2}$ emissions caused through a change in electricity demand: the average emission factor (AEF), the marginal power mix (MPM), the marginal system response (MSR) and an energy-model-derived marginal power plant (MPP). For generic and battery storage systems, a linear optimization on two levels optimizes the economic and environmental storage dispatch for a set of 50 small and medium enterprises in Germany. The four different emission factors have different signaling effects. The AEF leads to the lowest CO$_{2}$ reduction and allows for roughly two daily cycles. The other EFs show a higher volatility, which leads to a higher utilization of the storage system from 3.4 to 5.4 daily cycles. The minimum mean value for CO$_{2}$ abatement costs over all 50 companies is 14.13 €/t$_{CO}$$_{2}$

Onshore wind energy in Baden-Württemberg: a bottom-up economic assessment of the socio-technical potential

Detailed information about the potentials and costs of renewable energies is an important input factor for energy system models, as well as commercial and political decision-making processes. With its increasing locally installed capacity and hub height, wind energy plays an important role when it comes to meeting climate targets and optimizing electricity networks. Recently however, wind energy has faced more and more social barriers and land use constraints which can negatively impact both political goals and investment decisions. Therefore this work presents a bottom-up methodology based on graph-theoretical considerations to account for social barriers to estimate the socio-technical potential and the associated costs on a wind farm level. Calculations are conducted for the German federal state of Baden-Württemberg as a case study and are based on high resolution land use and wind speed data, using an algorithm to place wind parks by considering further constraints relating to land use planning. The socio-technical potential is found to be less than half that of previous studies that neglect these constraints, i.e. between 11.8 and 29.4 TWh, with costs between 7 and 14 €ct/kWh. A sensitivity analysis reveals a strong dependency of the overall socio-technical potential as well as its distribution across the federal state. In order to test the quality of the algorithm, already existing and planned wind parks were compared to modeled wind park locations and a very good correlation could be observed. The focus in future work should lie on the development of an economic criterion, which unlike the LCOE is able to account for the system costs of a widespread wind energy development, including network expansion, balancing power and reserve energy costs

A Cost-Effective and Transferable Methodology for Rooftop PV Potential Assessment in Developing Countries

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Industrial energy efficiency: Interdisciplinary perspectives on the thermodynamic, technical and economic constraints

Overreliance on energy from fossil fuels is unsustainable because of their regional depletion and associated environmental impacts. The British industrial sector accounts for around one fifth of final energy demand and one third of carbon emissions nationally. This thesis attempts to quantify the potential for industrial energy efficiency from the current baseline, by adopting thermodynamic and economic perspectives. The methodology involves a top-down analysis of energy trends within the manufacturing sector to determine the baseline against which changes are measured, leading to bottom-up case studies which explicitly consider the detailed mechanisms affecting energy demand. Top-down analysis highlights the diversity between industrial sectors, for which a sectoral classification based on process homogeneity is proposed. It also enables the long term, systemic potential for efficiency improvements to be estimated and identifies the barriers to uptake. Bottom-up case studies are better suited to identifying the sectoral potential in the short to medium term. Firstly, the technical potential for heat recovery from industrial sectors is quantified by recourse to thermodynamic quality and spatial considerations. Secondly, an energy and exergy analysis of a glass furnace enables a distinction between avoidable and unavoidable losses, leading to the identification of economic savings. Thirdly, a process integration study at a pulp and paper mill based on a pinch analysis and optimisation of a heat exchanger network highlights economic efficiency improvements. This thesis demonstrates that realising the full industrial energy efficiency potential requires improvements to public policy intended to overcome market-related barriers, especially the EU Emissions Trading Scheme and the Carbon Trust, with additional scope for a mandatory efficiency standard relating to motors. Energy efficiency has to part of a company’s overall strategy to be effective. Future work should focus on heterogeneous sectors and the broader effects on industrial energy efficiency of globalisation and the shift towards services.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Analysing long-term opportunities for offshore energy system integration in the Danish North Sea

Acknowledgment The authors gratefully acknowledge the financial support of the Danish Hydrocarbon Research and Technology centre (DHRTC) for funding this research in the context of the “Alternative use of offshore infrastructures and reservoirs” program. Any remaining errors are the authors’ responsibility.Peer reviewedPublisher PD