402 results found
Acha Izquierdo S, Shah N, Bos J, Cost effective low carbon store analysis and replication, CIBSE Technical Symposium 2015
This paper explores how low carbon buildings can be easily and cost effectivelyreplicated for a commercial retailer. The analysis investigates zero carbonsupermarkets using bio-methane combined heat and power (CHP). Results showthat CHP & district heating is the most cost effective design for a sustainablesupermarket. However, its implementation depends greatly on third parties and thusis not easy to replicate. The second best alternative is to use a CHP coupled with anOrganic Rankine Cycle (ORC) when the buildings heat-to-power ratio is below 0.5.Otherwise, a CHP with no heat recovery solution is deemed best. Overall, the mostcost effective ZCS projects are the ones implemented in stores with a high heat-topowerratio, high energy intensities and large surface floor area.
Acha S, van Dam KH, Keirstead J, et al., Integrated modelling of agent-based electric vehicles into optimal power flow studies, Frankfurt, Germany
Heuberger CF, Staffell I, Shah N, et al., Levelised Value of Electricity - A Systemic Approach to Technology Valuation, 26th European Symposium on Computer Aided Process Engineering - ESCAPE 26
Howard BN, acha Izquierdo S, polak J, et al., Measuring building occupancy through ICT data streams, ECEEE SUMMER STUDY PROCEEDINGS
Lockwood FC, Shah NG, EVALUATION OF AN EFFICIENT RADIATION FLUX MODEL FOR FURNACE PREDICTION PROCEDURES.
A radiation model of the so-called 'flux' or 'differential approximation' variety is described. A six-term Taylor's series expansion is used to represent the directional dependency of intensity. This is substituted into the transfer equation which is then integrated over selected angles to yield a closed set of partial differential equations; the angles of integration are adjustable parameters of the model. Comparisons are presented between the predictions of the model and analytic solutions, some of which are new, for one- and two-dimensional geometries. Optimum values of the adjustable parameters are recommended. It is demonstrated that the discontinuous intensity distribution which typically occur at wall boundaries represents the major source of errors of any flux model in which the number of equations solved is limited by realistic economic considerations. A new boundary treatment is described which significantly reduces the error.
Samsatli S, Samsatli NJ, Shah N, Optimal design and operation of integrated wind-hydrogen-electricity networks for decarbonising the domestic transport sector in Great Britain, Fuel Cell & Hydrogen Technical Conference 2015
Samsatli S, Samsatli NJ, Shah N, Addressing the challenge of modelling energy storage in a whole energy system, wholeSEM Conference 2014
Samsatli S, Samsatli NJ, Shah N, Biomass Value Chain Model: a comprehensive and flexible toolkit for whole system biomass analysis and optimisation, International Bioenergy Conference 2014
Samsatli S, Samsatli NJ, Shah N, Spatio-temporal energy system models, . wholeSEM - DECC Workshop 2014
Samsatli S, Samsatli NJ, Shah N, Optimal design of a future hydrogen supply chain using a multi-timescale and spatially-distributed model, Hydrogen & Fuel Cell SUPERGEN Researcher Conference 2014
Samsatli S, Samsatli NJ, Shah N, Multi-time scale modelling and analysis of the future hydrogen supply chain, All-Energy Conference 2013
Samsatli S, Samsatli NJ, Shah N, Whole-system optimisation of integrated wind-electricity-hydrogen networks for decarbonising the domestic transport sector in Great Britain, World Hydrogen Technical Conference 2015
Samsatli S, Shah N, Fuel cell and hydrogen systems, Hydrogen and Fuel Cell Research Meeting 2014
Al-Ansari T, Korre A, Nie Z, et al., 2017, Integration of greenhouse gas control technologies within the energy, water and food nexus to enhance the environmental performance of food production systems, JOURNAL OF CLEANER PRODUCTION, Vol: 162, Pages: 1592-1606, ISSN: 0959-6526
Bhave A, Taylor RHS, Fennell P, et al., 2017, Screening and techno-economic assessment of biomass-based power generation with CCS technologies to meet 2050 CO2 targets, APPLIED ENERGY, Vol: 190, Pages: 481-489, ISSN: 0306-2619
Cardenas-Fernandez M, Bawn M, Hamley-Bennett C, et al., 2017, An integrated biorefinery concept for conversion of sugar beet pulp into value-added chemicals and pharmaceutical intermediates, FARADAY DISCUSSIONS, Vol: 202, Pages: 415-431, ISSN: 1359-6640
Chen W, Sharifzadeh M, Shah N, et al., 2017, Implication of Side Reactions in Iterative Biopolymer Synthesis: The Case of Membrane Enhanced Peptide Synthesis, INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, Vol: 56, Pages: 6796-6804, ISSN: 0888-5885
Delangle A, Lambert RSC, Shah N, et al., 2017, Modelling and optimising the marginal expansion of an existing district heating network, Energy, Vol: 140, Pages: 209-223, ISSN: 0360-5442
© 2017 The Authors Although district heating networks have a key role to play in tackling greenhouse gas emissions associated with urban energy systems, little work has been carried out on district heating networks expansion in the literature. The present article develops a methodology to find the best district heating network expansion strategy under a set of given constraints. Using a mixed-integer linear programming approach, the model developed optimises the future energy centre operation by selecting the best mix of technologies to achieve a given purpose (e.g. cost savings maximisation or greenhouse gas emissions minimisation). Spatial expansion features are also considered in the methodology. Applied to a case study, the model demonstrates that depending on the optimisation performed, some building connection strategies have to be prioritised. Outputs also prove that district heating schemes' financial viability may be affected by the connection scenario chosen, highlighting the necessity of planning strategies for district heating networks. The proposed approach is highly flexible as it can be adapted to other district heating network schemes and modified to integrate more aspects and constraints.
Elahi N, Shah N, Korre A, et al., 2017, Multi-stage Stochastic Optimisation of a CO<inf>2</inf> Transport and Geological Storage in the UK, Pages: 6514-6525, ISSN: 1876-6102
© 2017 The Authors. Deterministic whole system multi-stage optimisation frameworks provide valuable insights into the cost effective design and operation of CO 2 capture and storage (CCS) systems. However commercial deployment of CCS faces significant technical and economic uncertainties, which necessitate flexibility in system development strategies as well as coordination of all aspects of a CCS system across both time and space. This paper builds on a whole system dynamic CCS optimisation tool developed at Imperial College and presents a mixed integer linear programming approach for multi-stage multi-scenario stochastic optimisation of a spatially explicit integrated CCS system under uncertainty. The model provides great advantages through flexible strategies for all potential system state changes at every stage and early one-fit-for-all investment solutions that minimise financial loss and offer operational flexibility. The model is showcased through a case study set in the UK between 2015-2050 focusing on the techno-economic performance of the CCS value chain and considering uncertainties in the financial market and the storage capacity within a portfolio of Southern North Sea saline aquifer and depleted oil and gas fields.
Farzad S, Mandegari MA, Guo M, et al., 2017, Multi-product biorefineries from lignocelluloses: a pathway to revitalisation of the sugar industry?, BIOTECHNOLOGY FOR BIOFUELS, Vol: 10, ISSN: 1754-6834
Foster E, Contestabile M, Blazquez J, et al., 2017, The unstudied barriers to widespread renewable energy deployment: Fossil fuel price responses, ENERGY POLICY, Vol: 103, Pages: 258-264, ISSN: 0301-4215
Hankin A, Shah N, 2017, Process exploration and assessment for the production of methanol and dimethyl ether from carbon dioxide and water, Sustainable Energy Fuels, Vol: 1, Pages: 1541-1556
Heuberger CF, Rubin ES, Staffell I, et al., 2017, Power capacity expansion planning considering endogenous technology cost learning, Applied Energy, Vol: 204, Pages: 831-845, ISSN: 0306-2619
© 2017 Elsevier Ltd We present an power systems optimisation model for national-scale power supply capacity expansion considering endogenous technology cost reduction (ESO-XEL). The mixed-integer linear program minimises total system cost while complying with operational constraints, carbon emission targets, and ancillary service requirements. A data clustering technique and the relaxation of integer scheduling constraints is evaluated and applied to decrease the model solution time. Two cost learning curves for the different power technologies are derived: one assuming local learning effects, the other accounting for global knowledge spill-over. A piece-wise linear formulation allows the integration of the exponential learning curves into the ESO-XEL model. The model is applied to the UK power system in the time frame of 2015 to 2050. The consideration of cost learning effects moves optimal investment timings to earlier planning years and influences the competitiveness of technologies. In addition, the maximum capacity build rate parameter influences the share of power generation significantly; the possibility of rapid capacity build-up is more important for total system cost reduction by 2050 than accounting for technology cost reduction.
Heuberger CF, Staffell I, Shah N, et al., 2017, A systems approach to quantifying the value of power generation and energy storage technologies in future electricity networks, Computers & Chemical Engineering, ISSN: 0098-1354
Heuberger CF, Staffell I, Shah N, et al., 2017, An MILP Modeling Approach to Systemic Energy Technology Valuation in the 21<sup>st</sup> Century Energy System, Pages: 6358-6365, ISSN: 1876-6102
© 2017 The Authors. New cannot be measured with old. The transformation of the electricity system from a network of fossil-based dispatchable power plants to one with large amounts of intermittent renewable power generation, flexible loads and markets, requires a concurrent development of new evaluation tools and metrics. The focus of this research is to investigate the value of power technologies in order to support decision making on optimal power system design and operation. Technology valuation metrics need to consider the complexity and interdependency of environmental and security objectives, rather than focusing on individual cost-competitiveness of technologies outside of the power system. We present the System Value as a new technology valuation metric, based on a mixed-integer linear program (MILP) formulation of a national-scale electricity system. The Electricity System Optimization model is able to capture detailed technical operation of the individual power plants as well as environmental and security requirements on the system level. We present a case study on the System Value of onshore wind power plants in comparison with Carbon Capture and Storage (CCS) equipped gas-fired power plants in a 2035 UK electricity system. Under the given emission constraints, the deployment of both technologies reduce total system cost of electricity generation. In the case of CCS-equipped power plants the reductions in total system cost are 2 to 5 times higher than for the deployment of onshore wind capacity.
Heuberger CF, Staffell I, Shah N, et al., 2017, The changing costs of technology and the optimal investment timing in the power sector
Heuberger CF, Staffell I, Shah N, et al., 2017, What is the Value of CCS in the Future Energy System?, Pages: 7564-7572, ISSN: 1876-6102
© 2017 The Authors. Ambitions to produce electricity at low, zero, or negative carbon emissions are shifting the priorities and appreciation for new types of power generating technologies. Maintaining the balance between security of energy supply, carbon reduction, and electricity system cost during the transition of the electricity system is challenging. Few technology valuation tools consider the presence and interdependency of these three aspects, and nor do they appreciate the difference between firm and intermittent power generation. In this contribution, we present the results of a thought experiment and mathematical model wherein we conduct a systems analyses on the effects of gas-fired power plants equipped with Carbon Capture and Storage (CCS) technology in comparison with onshore wind power plants as main decarbonisation technologies. We find that while wind capacity integration is in its early stages of deployment an economic decarbonisation strategy, it ultimately results in an infrastructurally inefficient system with a required ratio of installed capacity to peak demand of nearly 2. Due to the intermittent nature of wind power generation, its deployment requires a significant amount of reserve capacity in the form of firm capacity. While the integration of CCS-equipped capacity increases total system cost significantly, this strategy is able to achieve truly low-carbon power generation at 0.04 t CO2 /MWh. Via a simple example, this work elucidates how the changing system requirements necessitate a paradigm shift in the value perception of power generation technologies. Published by Elsevier Ltd.
Khor CS, Elkamel A, Shah N, 2017, Optimization methods for petroleum fields development and production systems: a review, Optimization and Engineering, Pages: 1-35, ISSN: 1389-4420
© 2017 Springer Science+Business Media, LLC In this review, we survey the widespread use of numerical optimization or mathematical programming approaches to develop and produce petroleum fields for design and operations; lift gas and rate allocation; and reservoir development, planning, and management. Early applications adopted linear programming alongside heuristics. With continuous advancements in computing speed and algorithms, we have been able to formulate more complex and meaningful models including nonlinear programs and mixed-integer linear and nonlinear programs. Various formulations and solution strategies have been used including continuous and discrete optimization, stochastic programming to handle uncertainty, and metaheuristics such as genetic algorithms to increase solution quality while reducing computational load.
Kong Q, Shah N, 2017, Development of an Optimization-Based Framework for Simultaneous Process Synthesis and Heat Integration, INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, Vol: 56, Pages: 5000-5013, ISSN: 0888-5885
Kucherenko S, Klymenko OV, Shah N, 2017, Sobol’ indices for problems defined in non-rectangular domains
© 2017 The Authors A novel theoretical and numerical framework for the estimation of Sobol’ sensitivity indices for models in which inputs are confined to a non-rectangular domain (e.g., in presence of inequality constraints) is developed. Two numerical methods, namely the quadrature integration method which may be very efficient for problems of low dimensionality and the MC/QMC estimators based on the acceptance-rejection sampling method are proposed for the numerical estimation of Sobol’ sensitivity indices. Several model test functions with constraints are considered for which analytical solutions for Sobol’ sensitivity indices were found. These solutions were used as benchmarks for verifying numerical estimates. The method is shown to be general and efficient.
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