**2. Emerging Tools**

This section discusses the modelling, simulation, optimisation, and assessment studies aimed at improving heat integration and heat transfer, integrated and newly developed heat exchangers, integration of renewables, and economic and environmental sustainability. One-third of energy is lost in the form of waste heat, as reported by [41]. According to the analysis by Papapetrou et al. [42], the total waste heat potential in EU is about 300 TWh/y, of which one-third is low-temperature waste heat, 25% occurs between 200–500 ◦C, and the remainder occurs above 500 ◦C. Bianchi et al. [43] suggested the theoretical potential of the EU's thermal energy waste was 920 TWh/y and 279 TWh of Carnot potential. This highlight the important roles of waste heat recovery in enhancing energy efficiency and emission reduction.

Heat integration [44] and heat transfer intensification [45] are long-standing tools for reducing energy consumption. However, they are continuing to be valuably extended. They have supported a significant issue, namely the reduction of the cost of energy transmitted to the cost of products and services. A substantial amount of effort has been made in making energy cleaner. However, the cleanest energy is that saved and consequently not produced [46]. These issues are closely related to environmental footprints, particularly carbon footprints. These should more precisely be named carbon emissions footprints and, more comprehensively, greenhouse gas footprints, including other greenhouse gases in addition to CO2 [47]. The most important is, in addition to CO2, CH4 and water vapour. To a lesser extent, but still significant, are surface-level ozone, NOx, and fluorinated gases, because all of these also involve infrared radiation [48].

However, all of the mentioned tools would not be possible without heat exchangers [49]. Heat exchangers are an important component in most plants, and they are also used in motor vehicles and airplanes. Their efficiency and cost-to-energy-saved ratio are important for their value to modern design. They have been continuously developed from their advent during the industrial revolution until the highly sophisticated pieces of equipment of the present [50]. A notable development comprises a modern plate and compact heat exchangers performing at low ΔTmin, which are able to reduce low potential waste heat. In Northern China, for example, this amounts to 100 Mt standard coal equivalents (Mtce, 2.93 EJ) and throughout steel mills in Hebei province it reaches 44,268 MW and in cement factories 2155 MW [51]. These issues were addressed by several papers in the SI, e.g., [52].

Renewables implementation remains a challenge despite the fact that their economic feasibility is reported to be improving (see Section 1). The technical challenges encountered arise mainly from the reliability of supply, facilities for transmission and distribution networks, connectivity to the existing grid, and storage. Modelling and simulation studies facilitate the understanding of the energy system (time profile scale and uncertainty, conditions, limitations) and predict performance in the real world for a more reliable integrated design. Different methods exist to address renewable uncertainty, for example, stochastic programming, fuzzy theory, robust programming, chance-constrained programming [53] and point estimate method [54]. Mehrjerdi and Rakhshani [55] modelled the correlation of time scale and uncertainty in an energy management system and incorporated load and wind energy uncertainties using mixed-integer stochastic programming. Talaat et al. [56] integrated wave, solar, and wind energy in a study in which the change of different environmental conditions was considered via simulation using Simulink. Baum et al. [57] assessed the intermittency mitigation potential of a dynamic, active demand response method in a smart grid using Monte Carlo simulation. Simulation software for a power system using intermittent energy sources was demonstrated by Fiedler [58] based on weather data in Australia. The advantages of diversification compared to dependence on a mono-system were highlighted. Draycott et al. [59] reviewed approaches to replicating the ocean environment, which is relatively complex, for an offshore renewable energy simulation (physical and numerical). Conducting such simulations is important prior to costly full-scale wave, tidal energy development. Long-range energy alternatives planning system (LEAP) and MARKAL simulations have also been used as forecasting models in various energy planning studies [60].

Optimisation studies of renewable energy are relatively broad, and coverage can range from micro (efficiency, material) to macro (regional planning, distribution design) aspects. An example of optimisation studies from a micro perspective is the optimisation of biomass blends for syngas production [61]. To enhance the energy efficiency of solar PV panels, Peng et al. [62] optimised their cooling performance and suggested the efficiency enhancement is up to 47%. Bravo et al. [63] assessed the integration of the calcium looping process as a thermochemical energy storage system in hybrid solar power plants. Macro-level optimisation focuses on distribution planning or design. For example, Zheng et al. [64] optimised the design of a biomass integrated microgrid with demand-side management under uncertainty. Nowdeh et al. [65] proposed a method based on a multi-objective evolutionary algorithm to optimise the placement and sizing of photovoltaic panels and wind turbines in a distribution network. A similar study was conducted by Jafari et al. [66], but the objective function was to minimise pollution, financial, and reliability issues rather than to reduce loss and improve reliability. Rinaldi et al. [67], in contrast, optimised the allocation of PV and storage capacity considering consumer types and urban settings for Switzerland. Another stochastic mathematical model was proposed by Santibañez-Aguilar et al. [68] to specifically support PV manufacturing supply chain development. It is crucial to support overall sustainability by considering the potential to locally produce different PV elements. Because flexibility is an important element of an integrated renewable energy system, stochastic optimisation algorithms are one of the most commonly applied methods [69]. The Fuzzy -graph is another method that can be applied to optimise renewable energy utility systems, as used by Aviso [70] for the abnormal operation of an off-grid system. Various software tools for the planning of hybrid renewable energy systems, including HOMER, Calliope, RETScreen, DER-CAM, Compose, iHOGA, and EnergyPRO, were recently reviewed by Cuesta et al. [71]. Akhtari et al. [72] optimised hybrid renewable earth–air heat exchanger with an electric boiler, wind, PV, and hydrogen configuration and Amin Razmjoo et al. [73] optimised a distributed generation-based photovoltaic system using HOMER. The inclusion of social factors in software tools is suggested to further enhance the capability of the software packages in optimising design.

Analysis and assessment studies can act as monitoring tools to determine the current performance quantitatively for comparison between alternatives and identify possible improvements in design. Life cycle assessment (LCA) based on environmental impacts or environmental footprints [47] and techno-economic assessment [74] are among the common approaches. Khoshnevisan et al. [75] performed a consequential life cycle assessment to compare the conversion of the organic fraction of municipal solid waste to bioenergy and high-value bioproducts (e.g., microbial protein, lactic, and succinic acid). The environmental impact of energy production through anaerobic digestion of pig manure was quantified by Ramírez-Islas [76]. Eutrophication was identified as the most negative effect which required further attention. To simplify the LCA of solar heating and cooling technologies, Longo et al. [77] developed an Environmental Lifecycle Impacts of Solar Air-conditioning System (ELISA) tool to account for the energy and environmental impacts. The PV-assisted system was identified as having a better life cycle performance compared to thermal-driven solar heating and cooling and a conventional system (electric heat pump). Wang et al. (2020) identified the geothermal gradient as the key factor of environmental impacts, in which acidification, eutrophication, and global warming potential can be reduced by a large geothermal gradient. Life cycle sustainability assessment [78] has received increasing attention in recent years. This is similar to LCA, but more comprehensively represents sustainability, including consideration of social life cycle assessment and life cycle costing. Because of increasing concern regarding interrelationships, nexus analysis has been conducted to further understand sustainability, particularly relating to the water-energy nexus, as conducted by Duan and Chen [79]. Fan et al. [80] proposed a graphical analysis tool considering the emission–cost nexus for sustainable biomass utilisation. Various sustainability indicators have also been developed for decision making, e.g., sustainable energy development index [81] and other sustainability indicators for renewable energy systems reviewed by Liu [82].
