Within the context of the Smart City, the need for intelligent approaches to manage and coordinate the diverse range of supply and conversion technologies and demand applications has been well established. The wide-scale proliferation of sensors coupled with the implementation of embedded computational intelligence algorithms can help to tackle many of the technical challenges associated with this energy systems integration problem. Nonetheless, barriers still exist, as suitable methods are needed to handle complex networks of actors, often with competing objectives, while determining design and operational decisions for systems across a wide spectrum of features and time-scales. This review looks at the current developments in the smart energy sector, focussing on techniques in the main application areas along with relevant implemented examples, while highlighting some of the key challenges currently faced and outlining future pathways for the sector. A detailed overview of a framework developed for the EU H2020 funded Sharing Cities project is also provided to illustrate the nature of the design stages encountered and control hierarchies required. The study aims to summarise the current state of computational intelligence in the field of smart energy management, providing insight into the ways in which current barriers can be overcome.

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Decarbonising the built environment cost-effectively is a complex challenge public and private organisations are facing in their effort to tackle climate change. In this context, this work presents an integrated Technology Selection and Operation (TSO) optimisation model for distributed energy systems in commercial buildings. The purpose of the model is to simultaneously optimise the selection, capacity and operation of photovoltaic (PV) and battery systems; serving as a decision support framework for assessing technology investments. A steady-state mixed-integer linear programming (MILP) approach is employed to formulate the optimisation problem. The virtue of the TSO model comes from employing granular state-of-the-art datasets such as half-hourly electricity demands and prices, irradiance levels from weather stations, and technology databases; while also considering building specific attributes. Investment revenues are obtained from reducing grid electricity costs and providing fast-frequency response (FFR) ancillary services. A case study of a distribution centre in London, UK is showcased with the goal to identify which technologies can minimise total energy costs against a conventional system setup serving as a benchmark. Results indicate the best technology configuration is a combination of lithium-ion batteries and mono-crystalline silicon PVs worth a total investment of £1.72 M. Due to the available space in the facility, the preferred PV capacity is 1.76 MW, while the battery system has a 1.06 MW power capacity and a 1.56 MWh energy capacity. Although PV performance varies across seasons, the solution indicates almost 30% of the energy used on-site can be supplied by PVs while achieving a carbon reduction of 26%. Nonetheless, PV and battery systems seem to be a questionable investment as the proposed solution has an 8-year payback, despite a 5-year NPV savings of £300k, implying there is still a performance gap for such systems to be massively deployed across the UK. Overall, the TSO model provides valuable insights into real-world project evaluation and can help to reduce the uncertainty associated with capital-intensive projects; hence proving to be a powerful modelling framework for distributed energy technology assessments

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Managing the carbon footprint of companies and addressing their respective decarbonisation plans is a challenging endeavour. The aim of this study is to help companies' better understand the issues around decarbonisation and environmental performance by suggesting a holistic management process they could embark on. This process comprises two crucial steps which are: a) sustainability reporting and b) low-carbon roadmaps. These steps are covered and further developed based on a detailed study of the UK food retail sector. This sector is relevant due to its economical and environmental importance, but most importantly it has a significant record of available environmental reports in the public domain and a large potential to influence consumers, policy makers, and multiple supply chains. Sustainability reporting is assessed by analysing environmental KPIs disclosed in Corporate Social Responsibility (CSR) reports and then these are compared against industry standards. This analysis highlights a general lack of consistency and transparency in CSR reporting of UK food retailers. Consequently, a low carbon roadmap based on relevant KPIs and on the 'backasting' framework is presented as a case study in order to showcase how a hypothetical UK food retailer can employ a low carbon roadmap to meet strategic targets. The case study demonstrates that ambitious environmental targets are achievable if robust corporate action plans are followed and steered adequately to meet long term targets. Furthermore, the case study also indicates capital might be misallocated in favour of highly visible environmental stores and on-site energy generation technologies with expensive carbon abatement potential whilst in theory more could be done by applying cost-effective energy efficiency measures which have the potential to deliver substantial carbon savings. Highlights: General lack of consistency and transparency in CSR reporting of UK food retailer  In average, no real improvement of transparency in CSR reporting since 2005  Energy saving measures have a greater potential compared to on-site generation  A learning process from the lighthouse store results is required  Ambitious environmental targets are achievable

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