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This paper presents an analysis of providing a typical distillery with low carbon energy through the combination of local wind energy, solar PV, electricity storage and heat storage. The aim of this is to increase the sustainability of the energyintensive whisky industry. Using hourly local renewable resource data and typical distillery consumption information, the local energy generation is balanced against the demand at the time of use. This followed by load shifting using a battery and heat storage. Results show that significant carbon savings can be achieved by a carefully designed portfolio of hybrid generation, battery storage and heat storage.

Off-grid PV systems are providing critical access to energy services for millions of people throughout the globe. However, optimum sizing of these PV systems still poses a challenge, as inadequate system sizing could result in low system reliability and/or high cost of electricity generated. This paper presents a hybrid method of sizing off-grid PV systems for undefined electricity consumption. It then compares this sizing with off-grid PV systems installed in Nigeria – ranked the most populous electricity-deficit country in the world. The yields of the installed off-grid PV systems are also simulated for four major cities in Nigeria. Results show that for the over 1.5MWp of off-grid PV systems installed in the country, there is a potential 1.11 – 3.04 MWh of unutilised surplus electricity, which can supply 2hours of green electricity to at least 2,000 Tier-2 households during peak demand in the dry hot season. Thus, our hybrid model provides design and operational insight to off-grid PV system optimization.

With increasing decarbonisation and accessibility to our energy systems and markets, there is a need to understand and optimise the value proposition for different stakeholders. Game-theoretic models represent a promising approach to study strategic interactions between self-interested private energy system investors. In this work, we design and evaluate a game-theoretic framework to study strategic interactions between profit-maximising players that invest in network, renewable generation and storage capacity. Specifically, we study the case where grid capacity is developed by a private renewable investor, but line access is shared with competing renewable and storage investors, thus enabling them to export energy and access electricity demand. We model the problem of deducing how much capacity each player should build as a non-cooperative Stackelberg-Cournot game between a dominant player (leader) who builds the power line and renewable generation capacity, and local renewable and storage investors (multiple followers), who react to the installation of the line by increasing their own capacity. Using data-driven analysis and simulations, we developed an empirical search method for estimating the game equilibrium, where the payoffs capture the realistic operation and control of the energy system under study. A practical demonstration of the underlying methodology is shown for a real-world grid reinforcement project in the UK. The methodology provides a realistic mechanism to analyse investor decision-making and investigate feasible tariffs that encourage distributed renewable investment, with sharing of grid access.

Reliable access to electricity is still a challenge in many developing countries. Indeed, rural areas in sub-Saharan Africa and developing countries such as India still encounter frequent power outages. Local energy markets (LEMs) have emerged as a low-cost solution enabling prosumers with power supply systems such as solar PV to sell their surplus of energy to other members of the local community. This paper proposes a one-to-one automated negotiation framework for peer-to-peer (P2P) local trading of electricity. Our framework uses an autonomous agent model to capture the preferences of both an electricity seller (consumer) and buyer (small local generator or prosumer), in terms of price and electricity quantities to be traded in different periods throughout a day. We develop a bilateral negotiation framework based on the well-known Rubinstein alternating offers protocol, in which the quantity of electricity and the price for different periods are aggregated into daily packages and negotiated between the buyer and seller agent. The framework is then implemented experimentally, with buyers and sellers adopting different negotiation strategies based on negotiation concession algorithms, such as linear heuristic or Boulware. Results show that this framework and agents modelling allow prosumers to increase their revenue while providing electricity access to the community at low cost.

Renewable energy has achieved high penetration rates in many areas, leading to curtailment, especially if existing network infrastructure is insufficient and energy generated cannot be exported. In this context, Distribution Network Operators (DNOs) face a significant knowledge gap about how to implement curtailment rules that achieve desired operational objectives, but at the same time minimise disruption and economic losses for renewable generators. In this work, we study the properties of several curtailment rules widely used in UK renewable energy projects, and their effect on the viability of renewable generation investment. Moreover, we propose a new curtailment rule which guarantees fair allocation of curtailment amongst all generators with minimal disruption. Another key knowledge gap faced by DNOs is how to incentivise private network upgrades, especially in settings where several generators can use the same line against the payment of a transmission fee. In this work, we provide a solution to this problem by using tools from algorithmic game theory. Specifically, this setting can be modelled as a Stackelberg game between the private transmission line investor and local renewable generators, who are required to pay a transmission fee to access the line. We provide a method for computing the empirical equilibrium of this game, using a model that captures the stochastic nature of renewable energy generation and demand. Finally, we use the practical setting of a grid reinforcement project from the UK and a large dataset of wind speed measurements and demand to validate our model. We show that charging a transmission fee as a proportion of the feed-in tariff price between 15%-75% would allow both investors to implement their projects and achieve desirable distribution of the profit., Comment: Preprint of final submitted version

Electricity-access is a growing concern in developing countries of Sub-Saharan Africa where approximately 563million people (8% of the global population) lack electricity. Many use fossil-fuel generators and/or solar-home-systems (SHSs) with storage. Given the observed excess and unutilized electricity generated from these SHSs, a novel community peer-to-peer (P2P) electricity-market using automated negotiations is presented as a solution to improve access to electricity. Software agents representing World Bank Tiers 1 – 4 electricity-access households bilaterally negotiate electricity price and quantities applying five different negotiations heuristics. Simulation results show 65 – 100% trade of the excess generation; with Tiers 1 – 2 (low-income households) negotiating sufficient electricity (1 – 1.1kWh/day) at the least market-price of £0.35/kWh to move to Tier 3. Likewise, the Tier 3 household agent negotiated 1.5kWh/day sufficient to meet its needs. This novel electricitymarket model can help drive the United Nations goal of affordable, sustainable and modern energy for all by 2030.

Fluctuations in solar irradiance were varied in frequency and magnitude to investigate the performance of a directly-connected solar energy powered reverse osmosis (RO) membrane system. Typically, the system produced acceptable quality water with constant solar irradiances ranging from 400 to 1200 W m −2 . Low average motor powers were encountered during fluctuations, however, in many cases, good performance was still realised, even at solar irradiance values that were equivalent to −2 . This counter-intuitive result arises from the effect of averaging the motor power, with periods of high solar irradiance compensating for the under-performance at times when the system was off. Overall, even though the permeate flux was often low when operating under fluctuating conditions, the RO system continued to deliver satisfactory quality water and at a low specific energy consumption (SEC). Temporal studies revealed that a disruption of the concentration polarisation layer occurs via a naturally induced backwash for steps in the solar irradiance as low as 100 W m −2 . This suggests that a renewable energy powered RO filtration system could benefit from being operated from a fluctuating energy source. Furthermore, the operating conditions during the first couple of minutes after a system shutdown event is shown to be very important, with: (i) shorter off-periods resulting in good performance being achieved quicker, and (ii) short-term power availability dramatically improving system performance. These findings indicate that a renewable energy powered RO system can operate well from a fluctuating energy source, in particular when additional power – for example, via supercapacitor energy buffering – is available to boost the system after a shut-down period.

We present a new statistical wind forecasting tool based on Principal Component Analysis (PCA), which is trained on past data to predict the wind speed using an ensemble of dynamically similar past events. At the same time the method provides a prediction of the likely forecasting error. The method is applied to Meteorological Office wind speed and direction data from a site in Edinburgh. For the training period, the years 2008–2009 were used, and the wind forecasting was tested for the data from 2010 for that site. Different parameter values were also used in the PCA analysis to explore the sensitivity analysis of the results. The forecasting results demonstrated that the technique can be used to forecast the wind up to 24 h ahead with a consistent improvement over persistence for forecasting more than 10 h ahead. The comparison of the forecasting error with the uncertainty estimated from the error growth in the ensemble forecast showed that the forecasting error could be well predicted.

The personalized diagnosis, assistance and evaluation of students in open learning environments can be a challenging task, especially in cases that the processes need to be taking place in real-time, classroom conditions. This paper describes the design of an open learning environment under development, designed to monitor the comprehension of students, assess their prior knowledge, build individual learner profiles, provide personalized assistance and, finally, evaluate their performance by using artificial intelligence. A trial test has been performed, with the participation of 20 students, which displayed promising results.

The effect of wind turbine wakes in large offshore wind energy arrays can be a substantial factor in affecting the performance of turbines inside the array. Turbulent mixing plays a key role in the wake recovery, having a significant effect on the length over which the wake is strong enough to affect the performance of other turbines significantly. We highlight how turbulence affects wind turbine wakes using results from LES simulations of Lillgrund offshore wind farm in the context of SCADA data selected to mirror the wind conditions simulated. The analysis here concentrated on temporal spectra of wind velocities measured by the turbine's nacelle anemometer and calculated at the turbine locations in the computational model. The effect of the wind turbine rotor on the downstream flow is quantified by analysing the change in spectral features of turbines within the wind farm compared to turbines at the side of the farm exposed to the wind.

This paper provides a study of the impact of curtailment schemes, applied when generation exceeds demand, on the Capacity Factor (CF) of wind generators, including the effect of spatial wind correlation among different locations. Moreover, we discuss how a round-robin curtailment rule could be implemented to guarantee approximately equally curtailment ratio for generators of unequal rated capacity. Next, we consider a two-location problem, where excess renewable energy generation and demand are not co-located. We study the combined effect that curtailment schemes and line access rules have on the decision to invest in new transmission lines. In particular, we show that, for common access rules, this can lead to a Stackelberg game between transmission and local generation capacity investors, and we characterise the equilibrium of this game. Finally, we apply and exemplify our model to a concrete problem of building a transmission link in western Scotland, and we propose a mechanism for setting transmission charges that assures both that the transmission line gets built, but investors from the local community can also benefit from investing in renewable energy.

This study investigates the potential role of electric vehicles in an electricity network with a high contribution from variable generation such as wind power. Electric vehicles are modelled to provide demand management through flexible charging requirements and energy balancing for the network. Balancing applications include both demand balancing and vehicle-to-grid discharging. This study is configured to represent the UK grid with balancing requirements derived from wind generation calculated from weather station wind speeds on the supply side and National Grid data from on the demand side. The simulation models 1000 individual vehicle entities to represent the behaviour of larger numbers of vehicles. A stochastic trip generation profile is used to generate realistic journey characteristics, whilst a market pricing model allows charging and balancing decisions to be based on realistic market price conditions. The simulation has been tested with wind generation capacities representing up to 30% of UK consumption. Results show significant improvements to load following conditions with the introduction of electric vehicles, suggesting that they could substantially facilitate the uptake of intermittent renewable generation. Electric vehicle owners would benefit from flexible charging and selling tariffs, with the majority of revenue derived from vehicle-to-grid participation in balancing markets.

We present an analysis of the ability to predict the power output from a nominal wind turbine or wind farm a few hours ahead using only locally available data - either the current and recent wind speed or power output. A third method combines the current state with knowledge of the long-term climatology. The wind speed data were taken from a 46-year long record of hourly readings at a Scottish coastal site and converted to power output and electricity production using a generic wind turbine power curve. The wind speed data or the calculated power output at a given time were used to predict the output a few hours ahead, either using persistence, a linear model, or a model based on the mean daily cycle extracted from the long-term record. Since many wind farm operators base their forecast on current wind speed or output measures alone, this analysis will provide some quantification of the quality of this approach, either to help them plan their operation or be able to put these simple methods in quantitative context of more complex methods.

A microturbine is developed for harnessing energy from an airflow, which is generated by body motion. The energy should get transformed into usable electrical energy and thus provided for powering portable electrical devices. The turbine is flown axial and rotates in the same direction independent of the incoming airflow. Different designs of the turbine are tested and opposed to figure out the best parameters. For the best turbine, the integration and the process of walking is simulated.

Constant-temperature hot-film anemometry measurements in a two-layer system of a silicone fluid layer over a magnetic fluid layer has been investigated. Complicating factors potentially affecting the measurements were very slow velocities, the unusually high thermal conductivity of the magnetic liquid, the vicinity of solid boundaries, the presence of the interface, and the possibility of magneto-convection in addition to natural convection. All these factors required a calibration procedure combining both experimental calibration and a set of numerical models of the system. The findings obtained during the calibration were that neither natural convection nor magneto-convection were adversely affecting the measurements, probably due to the effects of the solid boundaries and the interface. It was furthermore observed that the fluid interface resulted in an effect analogous to the near-wall effect, but deformations of the interface due to the sensor or its mounting prevented measurements close to the boundary. Spatial variation of the fluid velocities affected the calibration of the sensor noticeably. While a simple calibration, either by moving the probe or by assuming a uniform velocity, can give a qualitatively reasonable velocity profile, it will quantitatively deviate significantly from the true velocities.

The energy planning problem becomes increasingly important during the recent years mainly due to the pressing needs that need to be taken into account in terms of economic, environmental and social aspects. At the same time, technology supports many options and alternative solutions in the design of energy and fuel supply chains (SCs), based on more holistic approaches that have their origin in the production management. Considering the introduction of an integrated framework of evaluation for alternative energy supply options, the present work applies a RTN (Resource Task Network) based representation of the Energy SCs (ESCs) along with a mathematical programming optimisation model in order to analyse and model a location specific energy planning problem. Special emphasis is given to fuel mix optimisation in terms of maximising the economic, social and environmental performance of ESCs. Furthermore, the interaction between aspects and implications under the existing limitations both in the design and technical characteristics of the system as well as in the environmental / air quality limitations, will be also revealed. On top of the results obtained, the present work’s added value relates to the development of the proposed model for the analysis of various energy planning scenarios, comprising in this way a very useful decision making tool.

We present here a computational fluid dynamics (CFD) simulation of Lillgrund offshore wind farm, which is located in the Oresund Strait between Sweden and Denmark. The simulation combines a dynamic representation of wind turbines embedded within a Large-Eddy Simulation CFD solver, and uses hr-adaptive meshing to increase or decrease mesh resolution where required. This allows the resolution of both large scale flow structures around the wind farm, and local flow conditions at individual turbines; consequently, the response of each turbine to local conditions can be modelled, as well as the resulting evolution of the turbine wakes. This paper provides a detailed description of the turbine model which simulates interactions between the wind, turbine rotors, and turbine generators by calculating the forces on the rotor, the body forces on the air, and instantaneous power output. This model was used to investigate a selection of key wind speeds and directions, investigating cases where a row of turbines would be aligned with the wind or at specific angles to the wind. Results shown include presentations of the spin-up of turbines, the observation of eddies moving through the turbine array, meandering turbine wakes, and an extensive wind farm wake several kilometres in length. The key measurement available for cross-validation with operational wind farm data is the power output from the individual turbines, where the effect of unsteady turbine wakes on the performance of downstream turbines was a point of interest. The results from simulations were compared to performance measurements from the real wind farm to provide a firm quantitative validation of this methodology. Having achieved good agreement between the model and actual wind farm measurements, the potential of the methodology to provide a tool for further investigations of engineering and atmospheric science problems is outlined., 48 pages, 36 figures

Management of industrial projects is an important subject that according to environmental issues and healthy of human beings should action in a sustainable route to avoid adverse effects of a project on environment. This necessity is even more viable in energy and petroleum industries such as crude oil transfer applications. Environmental Impact Assessment (EIA) is one of the best proved methods that survey different potential factors in a project and can suggest solutions for mitigating their impacts. EIA for repairing of pipeline is considered in terms of environmental issues, preserving equipments and using facilities effectively and efficiently. In this article, the overall trend of a case study is considered. The case study is about one of the main crude oil pipelines in Iran with 52″ diameter where the EIA study result is reported. Next, the best management strategies for the safe control of the most destructive impacts are also regarded.

Forces on solid objects passed through a magnetic liquid plug in a tube are measured. A simple one-dimensional model is developed based on hydrostatic and magnetic pressures. The results demonstrate its potential to be used to separate two fluids while allowing solids to pass from one fluid to the other.

A systematic analysis of the sensitivity of a wind turbine's output to changes in observed wind statistics between different sites in Scotland over available wind records of up to 43 years length was performed. The analysis was performed in the context of observed variability on time scales longer than a year. The findings are discussed in the context of the ability to predict the long-term wind energy potential reliably both for wind farms as well as small turbines. In the analysis, some measures are defined to quantify the forecast accuracy and the long-term prediction error. One of the items of discussion was motivated by the observation in the wind industry that the year 2010 was a poor year, with hopes that it was just an exceptional year and fears that it might be an indicator of continuing climate change. The result of this discussion is that 2010 can only be seen as an outlier if one assumes that the past decades represent a constant wind climate. A linear regression, however, suggests that this assumption may not be correct and that 2010 may have been a low-wind year but consistent with generally observed fluctuations around a changing wind climate.