Developing a performance evaluation scheme for engineering facilities in commercial buildings: state-of-the-art review

Various post-occupancy evaluation schemes have been introduced for assessing building performance but one tailored for large-scale commercial buildings remains to be seen. Intended to develop a scheme for evaluating the performance of engineering facilities in existing commercial buildings, a multi-stage study was carried out in a dense-built metropolis – Hong Kong. Reported here is the part of work based on an extensive literature review. Considering the characteristics of relevant evaluation schemes, the requirements for useful performance evaluation and the criteria for selecting key performance indicators (KPIs), an integrated process-hierarchy model was formed for identifying applicable indicators for the intended scheme. A total of 71 indicators, classified into five categories: (i) physical, (ii) financial, (iii) task and equipment related, (iv) environmental, and (v) health, safety and legal, were identified. Their representations and their empirical applications, which are helpful for the strategic management of existing buildings, are also described

Energy management considering multiple power markets and microgrid storage

The operational cost of a microgrid is significantly influenced by the response of storage systems and the complexities of the power market’s tariff structures. This paper addresses the challenges arising from the coexistence of new market entries and traditional tariffs, which contribute to a complex market environment. To tackle this issue, the paper establishes a microgrid market environment encompassing four types of tariffs. By modeling the response of electric storage and cold storage in a microgrid, the study formulates a non-linear mixed-integer optimization problem. Numerical studies are then conducted to verify the model and analyze market performance. The results reveal a trade-off in behavior among different market entries when optimizing the total cost of microgrid operation. These findings shed light on the complexities and trade-offs involved in microgrid operational cost optimization within a diverse market environment, offering valuable insights for market participants

Techno-economic assessment of wireless charging systems for airport electric shuttle buses

Flightpath 2050, the European Commission's vision for aviation, requires that the aviation industry achieves a 75 % reduction in CO2 emissions per passenger mile and airports become emission-free by 2050. Airport shuttle buses in the airfields are going to be electrified to reduce ground emissions. Simultaneously, the airfield movement space and time schedules are becoming more limited for adopting stationary charging facilities for electrified ground vehicles. Therefore, the dynamic wireless charging technology becomes a promising technology to help improve the stability of electrification of the airfield transport network. This paper proposes a techno-economic assessment of wireless charging, wired charging, and conventional technologies for electrifying airport shuttle buses. A bi-level planning optimisation approach combines the multi-objective Non-dominated Sorting Genetic Algorithm (NSGA-III) and mixed integer linear programming (MILP) algorithm to handle a large number of decision variables and constraints generated from the investigated problem. The airport shuttle bus transport is simulated through a multi-agent-based model (MABM) approach. Four case studies are analysed for illustrating the techno-economic feasibility of wireless charging technology for airport electric shuttle buses. The results show that the wireless charging technology enables the electric shuttle buses to carry smaller batteries while conducting the same as tasks conventional diesel/petrol vehicles and the bi-directional wireless charging technology could help mitigate the impact of electrification of shuttle buses on the distribution network.Engineering and Physical Sciences Research Council (EPSRC): EP/S032053/

A novel approach for utilizing waste heat resources in the steel industry

The efficient utilization of waste heat resources plays a pivotal role in enhancing energy efficiency and curbing carbon emissions. To address this, effective planning for waste heat recovery (WHR) utilization becomes imperative, guiding consumers in device installation and capacity allocation. This paper introduces a novel approach to WHR utilization planning, tailored specifically for steel factories, with the goal of achieving optimal WHR solutions. The approach automates device selection, capacity allocation, and operational strategies while considering their impact on the regular manufacturing processes of the factories to maximize overall benefits. Unlike existing methods, this approach introduces discrete capacity selection modeling, considering the constraints of the limited product range during device selection. A numerical study illustrates the effectiveness of the proposed model in delivering optimal WHR device selection, capacity allocation, and operational strategies under various economic conditions. These enhancements contribute to the increased practicality and realism of the proposed method in comparison to existing approaches

Profit maximization for large-scale energy storage systems to enable fast EV charging infrastructure in distribution networks

Large-scale integration of battery energy storage systems (BESS) in distribution networks has the potential to enhance the utilization of photovoltaic (PV) power generation and mitigate the negative effects caused by electric vehicles (EV) fast charging behavior. This paper presents a novel deep reinforcement learning-based power scheduling strategy for BESS which is installed in an active distribution network. The network includes fast EV charging demand, PV power generation, and electricity arbitrage from main grid. The aim is to maximize the profit of BESS operator whilst maintaining voltage limits. The novel strategy adopts a Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm and requires forecasted PV power generation and EV smart charging demand. The proposed strategy is compared with Deep Deterministic Policy Gradient (DDPG), Particle Swarm Optimization and Simulated Annealing algorithms to verify its effectiveness. Case studies are conducted with smart EV charging dataset from Project Shift (UK Power Networks Innovation) and the UK photovoltaic dataset. The Internal Rate of Return results with TD3 and DDPG algorithms are 9.46% and 8.69%, respectively, which show that the proposed strategy can enhance power scheduling and outperforms the mainstream methods in terms of reduced levelized cost of storage and increased net present value