Planning, Devolution and the Territorialisation of Wind Energy in the UK

The decarbonisation of energy systems draw a new set of stakeholders into debates over energy generation, engage a complex set of social, political, economic and environmental processes and impact at a wide range of geographical scales, including local landscape changes, national energy markets and regional infrastructure investment. This paper focusses on a particular geographic scale, that of the regions/nations of the UK (Scotland, Wales, Northern Ireland), who have been operating under devolved arrangements since the late 1990s, coinciding with the mass deployment of wind energy. The devolved administrations of the UK possess an asymmetrical set of competencies over energy policy, yet also host the majority of the UK wind resource. This context provides a useful way to consider the different ways in which geographies of "territory" are reflected in energy governance, such through techno-rational assessments of demand or infrastructure investment, but also through new spatially-defined institutions that seek to develop their own energy future, using limited regulatory competencies. By focussing on the way the devolved administrations have used their responsibilities for planning over the last decade this paper will assess the way in which the spatial politics of wind energy is giving rise to renewed forms of territorialisation of natural resources. In so doing, we aim to contribute to clarifying the questions raised by Hodson and Marvin (2013) on whether low carbon futures will reinforce or challenge dominant ways of organising relationships between the nation-state, regions, energy systems and the environment.

Biogas reforming using renewable wind energy and induction heating

While the benefits of renewable energy are well known and used to influence government policy there are a number of problems which arise from having significant quantities of renewable energies on an electricity grid. The most notable problem stems from their intermittent nature which is often out of phase with the demands of the end users. This requires the development of either efficient energy storage systems, e.g. battery technology, compressed air storage etc. or through the creation of demand side management units which can utilise power quickly for manufacturing operations. Herein a system performing the conversion of synthetic biogas to synthesis gas using wind power and an induction heating system is shown. This approach demonstrates the feasibility of such techniques for stabilising the electricity grid while also providing a robust means of energy storage. This exemplar is also applicable to the production of hydrogen from the steam reforming of natural gas.

Voltage over-scaling:A cross-layer design perspective for energy efficient systems

Today's multi-media electronic era is driven by the increasing demand for small multifunctional devices able to support diverse services. Unfortunately, the high levels of transistor integration and performance required by such devices lead to an unprecedented increase of on-chip power that significantly limits the battery lifetime and even poses reliability concerns. Several techniques have been developed to address the power increase, but voltage over-scaling (VOS) is considered to be one of the most effective ones due to the quadratic dependence of voltage on dynamic power consumption. However, VOS may not always be applicable since it increases the delay in all paths of a system and may limit high performance required by today's complex applications. In addition, application of VOS is further complicated since it increases the variations in transistor characteristics imposed by their tiny size which can lead to large delay and leakage variations, making it difficult to meet delay and power budgets. This paper presents a review of various cross-layer design options that can provide solutions for dynamic voltage over-scaling and can potentially assist in meeting the strict power budgets and yield/quality requirements of future systems. © 2011 IEEE.

Energy balance of SRC willow used for managing farmyard washings - how does it compare to a conventional wastewater treatment works?

The water and wastewater industry in the UK accounts for around 3% of total energy use and just over 1% of total UK greenhouse gas emissions. Targets for greenhouse gas emissions reduction and higher renewable energy penetration, coupled with rising energy costs, growing demand for wastewater services and tightening EU water quality requirements, have led to an increased interest in alternative wastewater treatment methods. The use of short rotation coppice (SRC) willow for the treatment of wastewater effluent is one such alternative, which brings with it the dual benefits of wastewater treatment and production of biomass for energy. In order to assess the effectiveness of SRC willow, it is important to analyse the overall energy balance in terms of energy input versus energy output. This paper carries out an energy life cycle analysis of a specific SRC willow plantation in Northern Ireland to which farmyard washings (dirty water) are applied. The system boundaries include the establishment, maintenance, and harvesting of the plantation, along with the transport and drying of the wood for biomass combustion. The analysis shows that the overall energy balance is positive, and that the direct and indirect energy demands are 12% and 8% of gross energy production respectively. The energy demands of the plantation are compared with the energy required to treat an equivalent nutrient load in a conventional wastewater treatment plant. While a conventional plant consumes 2.6 MJ/m3 , the irrigation system consumes 1.6 MJ/m3 and the net energy production of the scenario is 48 MJ/m3 .

Hierarchical demand response using dantzig-wolfe decomposition

Demand Side Management (DSM) programmes are designed to shift electrical loads from peak times. Demand Response (DR) algorithms automate this process for controllable loads. DR can be implemented explicitly in terms of Peak to Average Ratio Reduction (PARR), in which case the maximum peak load is minimised over a prediction horizon by manipulating the amount of energy given to controllable loads at different times. A hierarchical predictive PARR algorithm is presented here based on Dantzig-Wolfe decomposition. © 2013 IEEE.

Efficient predictive demand response using laguerre functions

Predictive Demand Response (DR) algorithms allow schedulable loads in power systems to be shifted to off-peak times. However, the size of the optimisation problems associated with predictive DR can grow very large and so efficient implementations of algorithms are desirable. In this paper Laguerre functions are used to significantly reduce the size of the optimisation needed to implement predictive DR, thus significantly increasing the efficiency of the implementation. © 2013 IEEE.

Net energy analysis of a solar combi system with Seasonal Thermal Energy Store

EU targets require nearly zero energy buildings (NZEB) by 2020. However few monitored examples exist of how NZEB has been achieved in practise in individual residential buildings. This paper provides an example of how a low-energy building (built in 2006), has achieved nearly zero energy heating through the addition of a solar domestic hot water and space heating system (“combi system”) with a Seasonal Thermal Energy Store (STES). The paper also presents a cumulative life cycle energy and cumulative life cycle carbon analysis for the installation based on the recorded DHW and space heating demand in addition to energy payback periods and net energy ratios. In addition, the carbon and energy analysis is carried out for four other heating system scenarios including hybrid solar thermal/PV systems in order to obtain the optimal system from a carbon efficiency perspective.

Managing the variability of supply in the European power system with high wind energy penetration

The power system of the future will have a hierarchical structure created by layers of system control from via regional high-voltage transmission through to medium and low-voltage distribution. Each level will have generation sources such as large-scale offshore wind, wave, solar thermal, nuclear directly connected to this Supergrid and high levels of embedded generation, connected to the medium-voltage distribution system. It is expected that the fuel portfolio will be dominated by offshore wind in Northern Europe and PV in Southern Europe. The strategies required to manage the coordination of supply-side variability with demand-side variability will include large scale interconnection, demand side management, load aggregation and storage in the concept of the Supergrid combined with the Smart Grid. The design challenge associated with this will not only include control topology, data acquisition, analysis and communications technologies, but also the selection of fuel portfolio at a macro level. This paper quantifies the amount of demand side management, storage and so-called ‘back-up generation’ needed to support an 80% renewable energy portfolio in Europe by 2050.

Wind generation and electricity demand during the severe winter of 2009/10 in Ireland

Throughout the world the share of wind power in the generation mix is increasing. In the All Island Grid, of the Republic of Ireland and Northern Ireland there is now over 1.5 GW of installed wind power. As the penetration of these variable, non-dispatchable generators increases, power systems are becoming more sensitive to weather events on the supply side as well as on the demand side. In the temperate climate of Ireland, sensitivity of supply to weather is mainly due to wind variability while demand sensitivity is driven by space heating or cooling loads. The interplay of these two weather-driven effects is of particular concern if demand spikes driven by low temperatures coincide with periods of low winds. In December 2009 and January 2010 Ireland experienced a prolonged spell of unusually cold conditions. During much of this time, wind generation output was low due to low wind speeds. The impacts of this event are presented as a case study of the effects of weather extremes on power systems with high penetrations of variable renewable generation.

Hierarchical Demand Response for Peak Reduction using Dantzig‐Wolfe Decomposition

Demand Response (DR) algorithms manipulate the energy consumption schedules of controllable loads so as to satisfy grid objectives. Implementation of DR algorithms using a centralised agent can be problematic for scalability reasons, and there are issues related to the privacy of data and robustness to communication failures. Thus it is desirable to use a scalable decentralised algorithm for the implementation of DR. In this paper, a hierarchical DR scheme is proposed for Peak Minimisation (PM) based on Dantzig-Wolfe Decomposition (DWD). In addition, a Time Weighted Maximisation option is included in the cost function which improves the Quality of Service for devices seeking to receive their desired energy sooner rather than later. The paper also demonstrates how the DWD algorithm can be implemented more efficiently through the calculation of the upper and lower cost bounds after each DWD iteration.

Hierarchical Demand Response for Peak Minimization Using Dantzig-Wolfe Decomposition

Demand response (DR) algorithms manipulate the energy consumption schedules of controllable loads so as to satisfy grid objectives. Implementation of DR algorithms using a centralized agent can be problematic for scalability reasons, and there are issues related to the privacy of data and robustness to communication failures. Thus, it is desirable to use a scalable decentralized algorithm for the implementation of DR. In this paper, a hierarchical DR scheme is proposed for peak minimization based on Dantzig-Wolfe decomposition (DWD). In addition, a time weighted maximization option is included in the cost function, which improves the quality of service for devices seeking to receive their desired energy sooner rather than later. This paper also demonstrates how the DWD algorithm can be implemented more efficiently through the calculation of the upper and lower cost bounds after each DWD iteration.

Local Storage Meets Local Demand: A Technical Solution to Future Power Distribution System

Future power systems are expected to integrate large-scale stochastic and intermittent generation and load due to reduced use of fossil fuel resources, including renewable energy sources (RES) and electric vehicles (EV). Inclusion of such resources poses challenges for the dynamic stability of synchronous transmission and distribution networks, not least in terms of generation where system inertia may not be wholly governed by large-scale generation but displaced by small-scale and localised generation. Energy storage systems (ESS) can limit the impact of dispersed and distributed generation by offering supporting reserve while accommodating large-scale EV connection; the latter (load) also participating in storage provision. In this paper, a local energy storage system (LESS) is proposed. The structure, requirement and optimal sizing of the LESS are discussed. Three operating modes are detailed, including: 1) storage pack management; 2) normal operation; and 3) contingency operation. The proposed LESS scheme is evaluated using simulation studies based on data obtained from the Northern Ireland regional and residential network.

A multi vector energy analysis for interconnected power and gas systems

This paper presents the first multi vector energy analysis for the interconnected energy systems of Great Britain (GB) and Ireland. Both systems share a common high penetration of wind power, but significantly different security of supply outlooks. Ireland is heavily dependent on gas imports from GB, giving significance to the interconnected aspect of the methodology in addition to the gas and power interactions analysed. A fully realistic unit commitment and economic dispatch model coupled to an energy flow model of the gas supply network is developed. Extreme weather events driving increased domestic gas demand and low wind power output were utilised to increase gas supply network stress. Decreased wind profiles had a larger impact on system security than high domestic gas demand. However, the GB energy system was resilient during high demand periods but gas network stress limited the ramping capability of localised generating units. Additionally, gas system entry node congestion in the Irish system was shown to deliver a 40% increase in short run costs for generators. Gas storage was shown to reduce the impact of high demand driven congestion delivering a reduction in total generation costs of 14% in the period studied and reducing electricity imports from GB, significantly contributing to security of supply.

Application of Volterra Equations to Solve Unit Commitment Problem of Optimised Energy Storage and Generation

Development of reliable methods for optimised energy storage and generation is one of the most imminent challenges in modern power systems. In this paper an adaptive approach to load leveling problem using novel dynamic models based on the Volterra integral equations of the first kind with piecewise continuous kernels. These integral equations efficiently solve such inverse problem taking into account both the time dependent efficiencies and the availability of generation/storage of each energy storage technology. In this analysis a direct numerical method is employed to find the least-cost dispatch of available storages. The proposed collocation type numerical method has second order accuracy and enjoys self-regularization properties, which is associated with confidence levels of system demand. This adaptive approach is suitable for energy storage optimisation in real time. The efficiency of the proposed methodology is demonstrated on the Single Electricity Market of Republic of Ireland and Northern Ireland.

Optimal day-ahead scheduling of integrated urban energy systems

An optimal day-ahead scheduling method (ODSM) for the integrated urban energy system (IUES) is introduced, which considers the reconfigurable capability of an electric distribution network. The hourly topology of a distribution network, a natural gas network, the energy centers including the combined heat and power (CHP) units, different energy conversion devices and demand responsive loads (DRLs), are optimized to minimize the day-ahead operation cost of the IUES. The hourly reconfigurable capability of the electric distribution network utilizing remotely controlled switches (RCSs) is explored and discussed. The operational constraints from the unbalanced three-phase electric distribution network, the natural gas network, and the energy centers are considered. The interactions between the electric distribution network and the natural gas network take place through conversion of energy among different energy vectors in the energy centers. An energy conversion analysis model for the energy center was developed based on the energy hub model. A hybrid optimization method based on genetic algorithm (GA) and a nonlinear interior point method (IPM) is utilized to solve the ODSM model. Numerical studies demonstrate that the proposed ODSM is able to provide the IUES with an effective and economical day-ahead scheduling scheme and reduce the operational cost of the IUES.