Optimisation of stand-alone hybrid energy systems supplemented by combustion-based prime movers
Introduction
Global energy demand is rising steadily as a consequence of population growth and higher living standards. Around 1.2 billion people (17% of the global population) live without electricity: of those, 22% are in developing countries where a grid connection is not readily available [1]. The continuous depletion of fossil fuel reserves, growing awareness of the environmental impact of power generation solely reliant on combustion [2], [3], and the remoteness of many communities [4], [5], [6], [7] are driving the development of more sustainable energy supply options. Photovoltaic (PV), solar thermal power plants, wind energy, as well as generators driven by combustion engines in hybridised power installations can be cost-effective choices in remote areas compared to grid connections [8], [9], [10]. However, amongst all the renewable energy systems, PV is the dominant configuration [11], [12], [13], [14], [15]. Wind energy may not be technically feasible at low wind speeds [16] and is more intermittent than PV [17], thus requiring the use of intelligent methods in many instances to predict availability [18] . PV systems are common in many stand-alone energy applications due to their lower maintenance requirements [19] and more straightforward applications [13]. However, with solar irradiance also being seasonal and intermittent [20], [21], [22], [23], PV systems need supplementation to increase the reliability of meeting electric loads. Whilst storing surplus generated power in batteries over periods of low (electric) demand remains widespread [24], the environmental impact of such methods also needs to be considered [25], [26]. Even so, energy storage media are routinely used alongside renewables to stabilize power output [27], [28], [29]. An alternative approach to solely relying on (long-term) energy storage via batteries in energy systems based on renewables only involves deploying hybridisation featuring other (backup) prime movers [17].
With the exception of distributed energy systems, which can also be supplemented by grid connections [30], [31], the data presented in Table 1 clearly shows the lack of work done in integrating waste heat recovery into hybridised stand-alone systems. This has occurred even enough there are numerous hybrid systems in practice [44] and has led to these systems involving combustion processes which suffer from low thermal efficiency. Improving the sustainability of such hybridised systems can be achieved through increasing renewable energy penetration [45] and overall fuel utilisation efficiencies so as to reduce fossil fuel consumption [46]. In combustion-driven (supplemental) prime movers like Internal Combustion Engines (ICEs) or Micro Gas Turbines (MGTs), the recovery of waste heat to meet local heating and cooling loads can achieve higher overall power plant efficiency [47] and fewer environmental pollutants [48], [49]. This results in stand-alone and distributed energy systems based on Combined Heat and Power (CHP) or Combined Cooling, Heating, and Power (CCHP), commonly known as cogeneration and trigeneration [50].
Table 2 presents the typical technical characteristics of different prime movers used in CHP and CCHP applications. In this context, Khatri et al. [47] conducted an experimental investigation on a laboratory scale ICE-based CCHP system. Their results showed that thermal efficiency increases from 33.7% to 86.2% when the engine is operated in CCHP mode, with a similar reduction in CO2 emissions from 0.308 kg CO2/kW h (power mode only) to 0.1211 kg CO2/kW h (CCHP mode). Lin et al. [55] also carried out a similar type of experiment, but for a slightly larger residential size CCHP system featuring an ICE. They reported a significant increase in overall energy efficiency and a reduction in CO2 emissions. In this regard, the higher temperature range typically associated with exhaust gas outlets for an MGT (200–650 °C) may make them more suited for CHP applications compared to an ICE. However, the major concern for an MGT is its low electrical efficiency (∼30%), which reduces significantly at part load or when using fuels with lower heating values. However, from an environmental perspective, an MGT produces 100 times less NOx emissions than a diesel engine [51]. Studies on a 100 kW MGT (with CHP) have found electrical efficiencies up to 29% in the 80–100 kW range, with primary energy savings and overall efficiency of about 23% and 74% (CHP), respectively, with substantially lower pollutants [56]. With this in mind, more research is warranted into the analysis of waste heat and its recovery when sizing hybridised energy systems (Table 1). This becomes more relevant when thermal (cooling or heating) loads can be met through waste heat recovery, rather than diverting the same (total) electric load for this purpose in power-only modes of operation. This study analyses the (secondary) waste heat potential incidentally generated in a hybridised stand-alone system meeting an electric load only.
The ability to optimise the Power Management Strategy (PMS), which governs device switching in energy systems, also impacts techno-economic feasibility [57], [58], [59]. Whilst there is excellent industry-based simulation software for systems sizing [60], these applications are not self-adaptive [19], nor do they readily account for device start-up transients [20]. These limitations, as well as the ability to integrate the effects of different Power Management Strategies (PMS) in simulations, have led to a number of intelligent techniques being used to size or optimise hybrid energy systems. These methods include Genetic Algorithms (GAs) [20], [39], [43], [61], [62], [63], [64], [65], [66], Particle Swarm Optimization (PSO) [67], [38], Simulated Annealing (SA) [68], [69], Tabu Search (TS) [68], Artificial Bee Swarm Optimization (ABSO) [70], Harmony Search (HS) [68], Monte Carlo Simulation [71], [72], and Artificial Neural Networks (ANN) [73]. Most energy system simulations then target optimising a single and multiple objective function(s), including Net Present Cost (NPC, $/lifetime), the Cost of Energy (COE, $/kW h), or reducing environmental impact (kg CO2 eq/kW h or kg CO2 eq/lifetime), whilst meeting a specific load reliability [74]. The Loss of Power Supply Probability (LPSP) is widely considered to be a reliability index when sizing PV/wind [43], [75], [76], [77] or PV/wind/hydrogen [20] systems when meeting an electrical load only. However, it has not been considered while using an ICE or MGT to supplement electrical power supply or in the context of also meeting a thermal load [38], [78], [79]. In this regard, the effectiveness of GA is considered higher than other types of optimisation tools like PSO for finding the global optimum of an objective function, in addition to being able to handle larger numbers of parameters [79]. Although Dufo-Lopez and Bernal-Agustin et al. [78] used GA and a multi-objective optimisation (levelised cost of energy and life cycle emissions) in a hybrid system (consisting of PV, wind turbines, diesel engines, and battery storage), they did not study the effects of transient start up for supplementary prime movers or waste heat generation, which would obviously show overall system potential in CHP and CCHP applications. In another study, Cristóbal-Monreal and Dufo-López [80] studied both single- and multi-objective optimisation (minimisation of system cost or its weight in PV/Batt/ICE stand-alone systems) using GA and reported the COE at 0.26€/kW h. However, they only considered monthly averaged solar irradiation and ambient temperature profiles. Ismail et al. [37] also investigated GA optimisation of a PV/MGT system; however, they did not simulate the system using a dynamic load spanning an entire year, but only considered up to one week. This limits consideration of seasonal effects (e.g. temperatures, solar irradiation) which affects both the amount of PV power generated and the operational efficiency of PV modules as well as combustion-based prime movers. With the efficiency of ICEs and MGTs being temperature dependent [52], the use of coarser (larger) time steps may affect their derived analyses. They also did not discuss the effects of various modelling methodologies such as the effects of temporal resolution, the minimum starting threshold of supplementary prime movers, or the effects of single versus tandem ICE/MGT (i.e. using 60 kW versus two 30 kW engines) on the optimised systems. Details of their PMS (switching algorithm) were also not reported.
Some of the aforementioned gaps are overcome by the present paper, which not only compares the optimisation of stand-alone PV/Diesel and PV/MGT systems, but also these two types of system compare to solely renewable systems (PV only). The original contributions of this study include integrating the effects of the transient start-up time for prime movers (i.e. ICE, MGT), considering the waste heat produced as a consequence of systems sized when meeting an electric load, examining the effects of prime mover scalability and their minimum cut-in threshold, as well as the particulars of the GA methodologies used. The study utilises MATLAB R2015b for system modelling and its GA Optimisation Toolbox. The study also integrates typical power (efficiency) profiles for an ICE (30 kW, 60 kW), MGT (30 kW, 65 kW) and meteorological data for solar irradiation, temperature, and wind speed (which both affect PV panel efficiency) as well as dynamic electrical demand profiles to the conceptual stand-alone energy system studied. These outcomes are achieved in the context of using battery storage to cover (only) the start-up threshold of each combustion-based prime mover. Batteries are not used in the present research for long-term (seasonal) energy storage, as reported in other studies into hybridised systems [24], [81], [34] or for systems based on PV alone [82].
Section snippets
Methodology
A block diagram of a conceptual hybrid energy system is shown in Fig. 1. PV arrays are used to provide the base load with combustion type supplementary prime movers: Internal Combustion Engine (ICE) or Micro Gas Turbine (MGT). The system is stand-alone with no access to grid power and is subject to dynamically varying renewables and load. In the simulations which follow, this system has been modelled to operate in one of three modes: PV/Batt (mode-I); PV/Batt/ICE and PV/Batt/MGT based on
Results and discussion
In this paper, mode-I, -II, and –III configurations have been analysed when meeting the dynamic load at a specified LPSP (0.01 ± 0.005). Table 4 shows various scenarios of optimised (single objective) hybrid energy systems in mode-II and –III configurations when compared to the baseline mode-I (PV/Batt) system. For the baseline scenario, PV along with battery storage is used to supply the necessary load demand. Systems sizing is based on the single objective COE ($/kW h), with the consequential
Conclusions
This paper has analysed the impact of different types of prime movers when used to hybridise PV/Batt systems (PV/Batt/MGT and PV/Batt/ICE). It presents an insight into the effects of simulation methodology and different hardware parameters on the optimisation of hybridised energy systems. Whilst the outcomes are based on a set of assumptions and methods and are not meant to identify the general merits or drawbacks of specific models of prime movers (or energy system hardware), the outcomes of
Acknowledgements
The research is facilitated with an Edith Cowan University (ECU) research infrastructure block grant. The corresponding author acknowledges ECU for awarding an Australian Government Research Training Program Scholarship (RTP) to pursue a PhD research program. The support of Western Power, a Western Australian State Government-owned corporation, is appreciated in facilitating access to electric load data for the simulations undertaken. Cummins South Pacific are also gratefully acknowledged for
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