• Energy Research

This paper presents a new approach to assess and improve the environmental and economic performance of integrated electricity and heat district energy systems in light of (i) optimal operation of available multi-energy components (e.g., cogeneration and heat pumps) and (ii) energy exchanges within the district through internal electricity and heat networks. The latter is particularly attractive as, instead of constraining energy flows within each building, it enables the production and consumption of energy in the most attractive locations (e.g., in buildings that can accommodate photovoltaic panels). The proposed methodology is formulated as a mixed integer linear programming problem, and demonstrated with a real UK district comprising internal electricity and heat networks and several buildings. The results highlight the attractiveness of optimising multiple energy vectors, particularly in light of energy exchanges between buildings, in economic and environmental terms.

A systematic resilience and flexibility analysis of future power systems to address the impacts of climate change and Renewable Energy penetration is becoming increasingly important, as it is expected to have a great effect on the demand and supply portfolios. Depending on the intrinsic characteristics of each power system, different aspects have to be considered in the analysis since this cannot be universal for all power systems. To highlight this, the paper presents two different case studies pertaining to the Great Britain and Cyprus networks respectively. Firstly, the resilience of the Great Britain transmission network to future demand and supply scenarios (2020, 2030 and 2050) is evaluated using a reduced version of the current Great Britain transmission network. Subsequently, the future flexibility requirements of the isolated network of Cyprus are appropriately benchmarked against future energy mix scenarios that involve conventional generation and renewable energy penetration.

Demand for flexibility in electricity systems and the transition to the Smart Grid is increasing opportunities for demand response (DR). However, there are many barriers which prevent the full potential of DR being realised. Unlocking of this potential, through identification of DR enablers, can be aided through systematic classification and analysis of DR barriers. To this end, while previous works mostly focused on individual aspects, this paper develops a comprehensive ‘socio-techno-economic’ review, classification and analysis of DR barriers and enablers in a Smart Grid context. This provides an intellectual framework which may be used to underpin further work on the study and integration of DR. DR barriers are classified as either fundamental (i.e., relating to intrinsic human nature/essential enabling technology) or secondary (i.e., relating to anthropogenic institutions/or system feedbacks). Fundamental barriers are defined as economic, social or technological, whilst secondary barriers relate to political regulatory aspects, design of markets, physical (electrical network) issues, or to general understanding of DR. Subsequently, associated enablers for the defined barriers are suggested. Consideration of technical and commercial/social aspects for both power system and information and communication technology (the “internet of things”) domains provides a foundational contribution to improve understanding of DR within the Smart Grid paradigm. Finally, the complexity resulting from connections between various barriers, enablers and the energy system generally, and the existence of the signature characteristics of complex systems is acknowledged and implications discussed.

This paper introduces an original methodology for the co-optimization of operating and investments costs associated with the deployment of active management strategies in power distribution networks. The resulting large-scale optimization problem is solved using Benders decomposition. The concept is demonstrated using a module-based generic distribution system model. Case studies for different active management strategies (involving generation curtailment, reactive power compensation and transformer tap changers) and various DG penetration and density levels are presented and analyzed.

This paper discusses optimal operation strategies of a cluster of microturbines (MTs) for electrical load-following applications. Cluster operation ensures higher operational flexibility, but raises the issue of taking into account the partial-load MT characteristics, in terms of energy efficiency and pollutant emissions. In particular, from experimental results the NO x and CO emissions exhibit nonlinear and to some extent complementary trends at different partial-load levels. Hence, individual optimizations of fuel consumption and emission reduction are first carried out in this paper to show the conflicting nature of such objectives. Then, multi-objective optimization is performed to directly determine the best-known Pareto front. For this purpose, a procedure based on evolutionary programming is illustrated and applied to a practical case study. The results point out the degree of trade-off that can be sought when minimizing the local environmental impact of such distributed energy systems.

A recent paper by Ferroni and Hopkirk (2016) asserts that the ERoEI (also referred to as EROI) of photovoltaic (PV) systems is so low that they actually act as net energy sinks, rather than delivering energy to society. Such claim, if accurate, would call into question many energy investment decisions. In the same paper, a comparison is also drawn between PV and nuclear electricity. We have carefully analysed this paper, and found methodological inconsistencies and calculation errors that, in combination, render its conclusions not scientifically sound. Ferroni and Hopkirk adopt ‘extended’ boundaries for their analysis of PV without acknowledging that such choice of boundaries makes their results incompatible with those for all other technologies that have been analysed using more conventional boundaries, including nuclear energy with which the authors engage in multiple inconsistent comparisons. In addition, they use out-dated information, make invalid assumptions on PV specifications and other key parameters, and conduct calculation errors, including double counting. We herein provide revised EROI calculations for PV electricity in Switzerland, adopting both conventional and ‘extended’ system boundaries, to contrast with their results, which points to an order-of-magnitude underestimate of the EROI of PV in Switzerland by Ferroni and Hopkirk.

Power systems have typically been designed to be reliable to expected, low-impact high-frequency outages. In contrast, extreme events, driven for instance by extreme weather and natural disasters, happen with low-probability, but can have a high impact. The need for power systems, possibly the most critical infrastructures in the world, to become resilient to such events is becoming compelling. However, there is still little clarity as to this relatively new concept. On these premises, this paper provides an introduction to the fundamental concepts of power systems resilience and to the use of hardening and smart operational strategies to improve it. More specifically, first the resilience trapezoid is introduced as visual tool to reflect the behavior of a power system during a catastrophic event. Building on this, the key resilience features that a power system should boast are then defined, along with a discussion on different possible hardening and smart, operational resilience enhancement strategies. Further, the so-called $\Phi \Lambda {E}\Pi $ resilience assessment framework is presented, which includes a set of resilience metrics capable of modeling and quantifying the resilience performance of a power system subject to catastrophic events. A case study application with a 29-bus test version of the Great Britain transmission network is carried out to investigate the impacts of extreme windstorms. The effects of different hardening and smart resilience enhancement strategies are also explored, thus demonstrating the practicality of the different concepts presented.

This paper presents a unified techno-economic and environmental mathematical model for market driven operation optimization of different Distributed Multi-Generation (DMG) options in district heating schemes. The identified concepts of DMG are capable of providing significant operational benefits in that they have the flexibility to respond to electricity market signals, which is particularly important in the presence of a less flexible and more intermittency dominated power system. At the same time, the formulation allows assessment of the environmental benefits under different scenarios. The optimization formulation cast as a mixed integer linear program (MILP), is capable to incorporate the use of different multi-energy technologies and explicitly model inter-temporal constraints that allow assessment of the benefits of thermal storage, amongst the others.