Integrated optimisation of photovoltaic and battery storage systems for UK commercial buildings

Abstract

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

Thermodynamic and thermal comfort optimisation of a coastal social house considering the influence of the thermal breeze

“…Recent optimisation studies have focused on the application of artifical neural networks for tool performance improvement [65] and novel mixed integer linear frameworks [66,67]. In terms of technologies, recent studies have focused on the integration of renewable energy [68,69] and thermal properties optimisation of state-of-the-art phase change materials (PCM) [70]. Most recently, Chen and Yang [71], by using a surrogate-based optimisation model and considering the improvement of energy performance and thermal comfort, investigated the influence of climate conditions on passively designed buildings, exploring optimal designs at different climates in mainland China.…”

Dual Battery Storage System: An Optimized Strategy for the Utilization of Renewable Photovoltaic Energy in the United Kingdom

“…The analysis of the study in [35] shows that the optimal management of hybrid PV and BESS connected with the utility grid resulted in reduced electricity costs. The study of the work in [36], developed a technology selection and operation (TSO) optimization model to find out the optimal selection of PV and battery systems in the commercial buildings. A multi-period mixed-integer linear program (MILP) model for the scheduling and planning of the PV-battery storage system to magnify the net cash flow was studied in [37,38].…”

Micro-Tracked CPV Can Be Cost Competitive With PV in Behind-The-Meter Applications With Demand Charges

“…Microgrid optimization occurs at three levels (i) control of voltage, current and power, (ii) control of power quality, and (iii) control of scheduling and economics, and a comprehensive review is provided by Meng et al (2016). Focusing here on optimal scheduling and economics, various approaches have been used including: minimizing annual net cost of operation (Azzopardi and Mutale, 2009), minimizing cost of electricity consumed in a commercial building (Marnay et al, 2008;Fina et al, 2017;Mariaud et al, 2017), optimal participation in an energy market (Celli et al, 2005), optimal integration of distributed wind generation into the grid (Zhou and Francois, 2011), optimal management of a rural microgrid (Zhang et al, 2012), optimal PV-battery system for demand charge or peak reduction (Hanna et al, 2014;Khalilpour and Vassallo, 2016;Park and Lappas, 2017;Parra et al, 2017;McLaren et al, 2018;Raoufat et al, 2018), and optimal PV-storage capacity in terms of grid impact and PV utilization rate (Brusco et al, 2016;Merei et al, 2016;Cervantes and Choobineh, 2018;Freitas et al, 2018;Raoufat et al, 2018). These papers focus on PV microgrids.…”