Christopher Lund

Despite their vulnerability to climate change, mining and refining industries significantly provide energy transition materials. However, if no mitigation is implemented, the demand growth will increase direct and indirect emissions in the critical mineral sector. This research aims to comprehensively evaluate the decarbonisation agenda in lithium, nickel, and cobalt industries by profoundly examining the performances, strategies, and risks associated with climate transition. This study examines company reports of 27 players that accounted for at least half of the global lithium, nickel, and cobalt production. This study uses content analysis methodology to reveal patterns in decarbonisation targets, performances and practices. More than two-thirds of the observed companies favour onsite solar and wind generation. At least one-third of the samples mention energy-efficient equipment and electric vehicle adoption. Despite having a well-crafted strategy to reduce operational emissions, more efforts are needed to reduce the value-chain emissions. This study highlights the need for improvement in the carbon inventory and disclosure of scope three emissions by utilising artificial intelligence and maintaining strategic partnerships with stakeholders. Political, economic, social, technological, legal, and environmental (PESTLE) analysis also revealed patterns and linkages among the current decarbonisation challenges and opportunities, of which the industries are vulnerable to demand fluctuation, rising from regulatory and technological changes. This study highlights the current dynamics in the critical mineral supply chain, which may affect decarbonisation strategies in the industry. This study also provides a holistic approach to offer empirical practice for the industries, allowing them to tailor their strategies.

Due to the intermittent and uncertain nature of emerging renewable energy sources in the modern power grid, the level of dispatchable power sources has been reduced. The contemporary power system is attempting to address this by investing in energy storage within the context of standalone microgrids (SMGs), which can operate in an island mode and off-grid. While renewable-rich SMGs can facilitate a higher level of renewable energy penetration, they also have more reliability issues compared to conventional power systems due to the intermittency of renewables. When an SMG system needs to be upgraded for reliability improvement, the cost of that reliability improvement should be divided among diverse customer sectors. In this research, we present four distinct approaches along with comprehensive simulation outcomes to address the problem of allocating reliability costs. The central issue in this study revolves around determining whether all consumers should bear an equal share of the reliability improvement costs or if these expenses should be distributed among them differently. When an SMG system requires an upgrade to enhance its reliability, it becomes imperative to allocate the associated costs among various customer sectors as equitably as possible. In our investigation, we model an SMG through a simulation experiment, involving nine distinct customer sectors, and utilize their hourly demand profiles for an entire year. We explore how to distribute the total investment cost of reliability improvement to each customer sector using four distinct methods. The first two methods consider the annual and seasonal peak demands in each industry. The third approach involves an analysis of Loss of Load (LOL) events and determining the hourly load requirements for each sector during these events. In the fourth approach, we employ the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) technique. The annual peak demand approach resulted in the educational sector bearing the highest proportion of the reliability improvement cost, accounting for 21.90% of the total burden. Similarly, the seasonal peak demand approach identified the educational sector as the most significant contributor, though with a reduced share of 15.44%. The normalized average demand during Loss of Load (LOL) events also indicated the same sector as the highest contributor, with 12.34% of the total cost. Lastly, the TOPSIS-based approach assigned a 15.24% reliability cost burden to the educational sector. Although all four approaches consistently identify the educational sector as the most critical in terms of its impact on system reliability, they yield different cost allocations due to variations in the methodology and weighting of demand characteristics. The underlying reasons for these differences, along with the practical implications and applicability of each method, are comprehensively discussed in this research paper. Based on our case study findings, we conclude that the education sector, which contributes more to LOL events, should bear the highest amount of the Cost of Reliability Improvement (CRI), while the hotel and catering sector’s share should be the lowest percentage. This highlights the necessity for varying reliability improvement costs for different consumer sectors.

Solar photovoltaic (PV) and wind power generation are key contributors to the integration of renewable energy into modern power systems. The intermittent and variable nature of these renewables has a substantial impact on the power system’s reliability. In time-series simulation studies, inaccuracies in solar irradiation and wind speed parameters can lead to unreliable evaluations of system reliability, ultimately resulting in flawed decision making regarding the investment and operation of energy systems. This paper investigates the reliability deviation due to modeling uncertainties in a 100% renewable-based system. This study employs two methods to assess and contrast the reliability of a standalone microgrid (SMG) system in order to achieve this goal: (i) random uncertainty within a selected confidence interval and (ii) splitting the cumulative distribution function (CDF) into five regions of equal probability. In this study, an SMG system is modeled, and loss of load probability (LOLP) is evaluated in both approaches. Six different sensitivity analysis studies, including annual load demand growth, are performed. The results from the simulations demonstrate that the suggested methods can estimate the reliability of a microgrid powered by renewable energy sources, as well as its probability of reaching certain levels of reliability.

There has been a conversation about developing electric vehicle industries in Indonesia and Australia. While many studies have identified the barriers and opportunities for developing the industry, limited studies have been conducted to estimate the demand and supply of critical minerals to produce electric vehicle batteries, particularly in Indonesia. This study investigates the future demand for lithium, nickel, and cobalt in Indonesia and Australia by considering multiple scenarios and technological options. The study highlights the importance of circular economic intervention, such as material substitution and recycling, to ensure a sustainable supply of these minerals. The result shows that the lithium, nickel, and cobalt reserves will be adequate for developing the domestic electric vehicle industry in Australia. The domestic production will consume between 0.4 per cent and 4.5 per cent of the available reserve. In Indonesia, domestic production will consume up to 1.2 per cent of the available nickel reserve. However, it will consume more than a quarter of cobalt reserve in a scenario where high-nickel cathode dominates the market. Indonesia might also need to import lithium. Therefore, the result emphasises the need to foster bilateral cooperation between Indonesia and Australia to develop a secure and resilient electric vehicle industry in the region. The study concludes that a multifaceted approach, including technological and policy advancement in sustainable consumption and production practices, is essential to mitigate climate change in these countries.

Loss of load probability (LOLP) and expected energy not served (EENS) are commonly used in electrical power systems to evaluate reliability. LOLP defined as the probability that available generation capacity will be inadequate to supply customer demand. EENS defined as the expected amount of energy not being served to consumers by the system during the period considered due to system capacity shortages or unexpected power outages. Loss of Load Frequency (LOLF) is referred to a number of loss of load (LOL) event happened in the operation life span of the SMG. Loss of Load Reduction (LOLR) is defined as the required reduction in LOLF to obtain a specific reliability level. While power systems are designed to minimize LOLP and EENS, this is constrained by the total cost: investment cost, operation and maintenance cost, and cost of customer interruption (CCI). This research considers Standalone Microgrid (SMG), also known as Autonomous Microgrid which only operates in off-grid mode and cannot be connected to wider electrical power system. When designing a 100 % renewable energy integrated SMGs, it is crucial to determine the cost-effective reliability level (CERL). The CERL occurs when the total cost is minimum. This research proposes an approach to calculate the CERL for a fully renewable SMG. An analytical formulation is proposed to represent the LOLR needed to obtain a specific reliability level as a function of the required size of reliability improvement alternatives. The CCI is evaluated using LOLF and EENS indices. Finally, the total cost of the SMG system is evaluated for each reliability level. Consequently, the total cost of the SMG system is expressed as a function of reliability levels, and the minimum value of total cost and the corresponding reliability level are evaluated. In this research, a Monte Carlo Simulation (MCS) approach is used to find hourly LOLF, considering 25 years (219,000 h) of SMG lifespan, regression analysis is used for an analytical formulation, and mixed integer linear programming (MILP) is used for the investment decision making based on a cost minimisation approach. The result demonstrates that the CERL of the SMG system evaluated in the case study is 98.71 %.
[Display omitted]
•Monte Carlo simulation is used for loss of load frequency over 25 years.•Cost-effective reliability level of SMG is evaluated using total cost curve.•The failure analysis is performed in a hybrid renewable energy SMG system.•Curve fitting technique is used to determine various equations.•To minimize total cost, SMG should be operated at 98.25 % reliability.

Energy storage systems (ESSs) can help to reduce the intermittency and uncertainty of renewable energy supplies in power systems. ESSs are critical components of renewable-rich standalone microgrids (SMGs) to balance power generation and load demand, which is referred to as reliability. To achieve the same level of reliability as conventional power systems for renewable-based SMGs, significant investment in ESSs is required. However, due to the high investment costs of ESSs, the installation of large ESSs will not result in an affordable solution for achieving renewable SMG at the required reliability. As a result, this paper proposes a new sharing concept for ESS, namely energy storage as a service (ESaaS), to be implemented across microgrids as a low-cost alternative for improving reliability. In the proposed ESaaS concept, microgrids can use ESS from an ESS provider as required for different timeframes such as monthly, weekly, or daily, depending on the renewable resources and load profile characteristics. In this paper, the use of ESaaS is investigated over a range of timeframes for a 100 % renewable-based SMG with photovoltaic (PV), wind turbine (WT), and ESS. The SMG reliability is evaluated using Monte Carlo simulation both before and after the ESaaS strategy has been implemented. To evaluate the ESaaS affordability in improving the reliability of an SMG, this paper proposes the criteria of marginal cost of reliability, which indicates the rate of additional investment amount per percentage of reliability improvement. The marginal cost of reliability combines the economic and technical aspects of ESaaS in one simple criterion for effective decision-making among investment strategies such as different timeframes of ESaaS or permanent ESS. The simulation results show the ESaaS based on daily contract results in a lower marginal cost of reliability for the case study. To validate the effectiveness of the proposed ESaaS approach using marginal cost of reliability, the levelized cost of electricity (LCOE) is also calculated for different strategies of reliability improvement. The results confirm that the lowest LCOE is obtained using the strategy that provides the lowest marginal cost of reliability for the case study. In addition, a sensitivity analysis is performed to assess the difference in marginal cost of reliability under various uncertainties associated with the installed capacity of PV and WT, and the cost of utilising the ESaaS.

Clustering historical electricity consumption data is very important for creating representative demand profiles for the planning and operation of the power grids. This paper investigates a multi-dimensional framework for data clustering, which takes scattering and separation metrics, as well as the number of clusters into account. A combination of wavelet mutation with the Invasive Weed Optimization (IWO) method for clustering features is proposed. One notable advantage of the IWO method over other metaheuristic optimization algorithms is its ability to dynamically adapt the number of weed colonies during the search process, resulting in improved exploration and exploitation of the search space. The proposed strategy is applied to cluster the electricity consumption data from a large municipal government center in Perth, Western Australia. The suggested method is then evaluated by comparing it with the well-known method in the literature, namely, the k-means technique. After the data clustering, the obtained results are implemented in the design of a multi-microgrid system under two different scenarios of cooperative and noncooperative modes. To evaluate the performance of the proposed method, the proposed method is implemented on the operational planning of a real multi-microgrid distribution system in Western Australia using linear programming to take the advantage of the mathematical-based solvers. After performing some investigations, the cooperative mechanism, where the microgrids have participated in supplying the demand of microgrids was found to yield to greater operational and investment cost minimimzation. In terms of numerical comparison, the total cost in the cooperative model is 6.5% lower than that in a non-cooperative situation.

This paper proposes a model for energy sharing of interconnected microgrids (MGs), mainly where some MGs are owned by an entity, such as the government, which is the case study in Western Australia (WA). In the proposed model, MGs are able to trade energy among themselves when some of them have surplus generation, and others have lack of generations to meet their demand; however, they are obliged to pay for the use of distribution network, called network charge, and the share of network loss due to this energy transaction. In doing so, the network loss is taken into account and calculated through a power flow. The possibility of energy trading with the main grid is also considered through the wholesale electricity market. Considering the uncertainty of Photovoltaic (PV) generation and load involved, the decision making to inject or import energy to/from the main grid as well as to trade between MGs is obtained through a bi-level linear optimization. In the upper level, the distribution network operator intends to manage the energy exchange between MGs and energy trading with upstream grid, while in the lower level, each MG attempt to minimize its operational cost relating to PV and energy storage system (ESS). Finally, the proposed method is applied to a real project in Western Australia.

The marginal cost of reliability improvement (MCRI) is a very useful measure to compare the cost-effectiveness of various standalone microgrid (SMG) systems. This measure helps in decision making on reliability level and imports and exports between SMGs. The MCRI can elucidate how a SMG system is going to deal with the change of reliability requirements by customers and energy traders. This paper proposes an MCRI evaluation algorithm for a microgrid (MG) over its 25-year lifespan. A case study is evaluated, which consists of renewable energy resources (RES) and a battery energy storage system (BESS) as reliability improvement (RI) alternatives. Two sensitivity analysis study are performed to answer the following research questions: What if is the cost of energy resources changes? and What if demand response (DR) is included as an alternative to RI. Furthermore, whether maximum reliability can be achieved with 100% renewable generating resources is also evaluated. The Monte Carlo Simulation (MCS) method is used to model the equipment failure. The linear regression approach is used to create an equation for loss of load reduction (LOLR), for the addition of resource mix as a function of LOLR and for the addition of individual RI alternatives. A Matlab optimization tool is used to find the MCRI.

The clustering of historical electricity consumption data is an effective means of developing representative load profiles for long-term energy planning. This paper presents a multi-dimensional approach for clustering, considering scattering and separation metrics and the number of clusters. A novel hybrid approach to solve the clustering function is also proposed: a combination of Invasive Weed Optimization (IWO) and wavelet mutation strategy. The hybrid method is applied to half-hourly metered electricity consumption data from the Civic Centre of a large local (municipal) government in Perth, Western Australia, to create representative seasonal load profiles. The novel clustering approach is then tested against the well-known k-means method using Davies-Bouldin and silhouette indices. In each seasonal clustered profile, the hybrid method is found to outperform the k-means method. The hybrid method has been identified as an effective clustering approach for analyzing the behavior of loads and assisting the identification of suitable energy efficiency initiatives.