(601a) A Mathematical Programming Approach for Integrating Distributed Urban Energy Systems and Iot

Nagore Sabio[*]^1,2, Evgenia Mechleri¹ and Harvey Arellano-García¹

¹Department of Chemical and Process Engineering, University of Surrey, Guildford, United Kingdom

²UCL Energy Institute, University College London, London, United Kingdom

Over fifty per cent of the world’s population now lives in urban areas and much of the future population growth will take place in cities [1]. The growing penetration of variable renewable energy sources (VREs) is increasing the need for flexibility in the energy service domain. In the UK, installed capacity of renewable energy has increased more than six times in the last 15 years. Although electricity still dominates renewable generation, renewable heat contribution has risen up to 20% share recently [2]. VREs are commonly connected to the distribution grid in addition to other distributed energy systems (DES), which increases the complexity of the distribution grid management. In this sense, the demand and business case for frameworks capable of integrating demand and supply and providing an optimal design and operation of urban energy systems is increasing.

DESs are a suite of on-site, grid-connected or stand-alone technology systems that can be sourced by different types of distributed energy resources, such as natural gas, biomass, solar energy, wind and waste heat amongst others. Such DES offer great advantages over centralized generation by offering end users a diversified fuel supply, higher efficiency and lower emissions. However, the integration of renewable energy resources (e.g. wind turbines, PV units) which depend completely on the unsteady weather condition increase the complexity of energy provision continuation within DESs. On the other hand, the energy demands of end-consumers always fluctuate hourly which may cause energy unbalance between supply and demand sides. Our framework brings weather data and sensor information into a virtual energy plant optimization model that connects supplier and consumer in order to optimize potential flexibility gaps arising from supply and demand mismatch. The system includes two optimization levels: one that allows for providing the optimal distributed energy system design, and a subsequent level that optimizes its operation. The problem is posed as a two-stage hybrid MILP optimization model combining flexibility analysis and optimal synthesis for integrating energy supply and demand where environmental information is added to each stage, informing the consumer and supplier of the carbon footprints of the options selected. The novelties of the present framework therefore are threefold. First, we propose a smart metering design that makes use of the current information technologies and communication network infrastructure, bringing IoT and mathematical programming at the urban consumer level. Second, our approach integrates electricity and heat demands within a single frameworks for urban DES optimization. And finally, we combine traditional mathematical programming approaches such as flexibility analysis, optimal network synthesis and information technologies within a single optimization framework combining IoT and urban DES.

[1] Kirstead , J. and Shah, N. (2013). Urban Energy Systems – An integrated approach. London: Routledge

[2] Digest if UK Energy Statistics (DUKES), Chapter 6: Renewable sources of energy. (2016). Department of Business, Energy and Industrial Strategy,UK Government. Retrieved 30 October 2016.

[3] Staffell, I., Brett D.J.L., Brandon, N.P. and Hawkes, A.D. (2015). Domestic microgeneration: renewable and distributed energy technologies, policies and economics. Oxford: Routledge.