• Energy Research
• Closed Access close
• Restricted close
• Open Source close
• Embargo close
• 7. Clean energy close
• 12. Responsible consumption close
• US close
• AU close

Solar PV and battery storage technologies are known to provide savings to customers in the form of reduced electricity charges. Currently, these savings are only determined for the volume component (kWh) and not the demand component (kW or kVA). As interest grows in commercial solar PV and battery storage installations, the need to predict demand charge reductions is great. The aim of this research is to determine, with accuracy and reliability, the ability of solar PV and battery storage technologies in reducing demand charges. Results have shown that when simulated against a commercial-scale electricity consumption profile solar PV was able to reduce the maximum demand across five electricity networks in Australia by 0.05–1.51%. When coupled with a 12 kWh battery storage an additional 1.31–2.02% reduction was experienced. Battery utilisation strategy was shown to be critical in yielding greater demand reduction from the battery storage. Notably, it was shown that in the Ergon Energy electricity network, battery storage was able to supply demand at 34% lower cost ($/kW) than the network was able to. The results detail the first instance of demand reduction evaluation of solar PV coupled with battery storage, focusing on physical and financial outcomes in an Australian context.

Abstract It has lately been suggested that nuclear power technologies could be used to mitigate potential global warming. Doing this would give nuclear power technology a new role, and would lead to its widespread deployment worldwide. When examined carefully several barriers to accomplishing this goal are evident, even should the uncertainties of global warming become reduced enough that it could be treated as an established fact. These barriers involve the need for alternative forms of nuclear energy, uranium resource limitations, technology development requirements and difficulties in widespread deployment of nuclear power plants. Overcoming the barriers may prove to be much more difficult than has been appreciated todate, and could strongly influence the future research and development agenda for nuclear and associated technologies.

Abstract Methane is responsible for 20% of the global warming resulting from greenhouse gas emissions. Municipal solid waste (MSW) landfills are the third largest anthropogenic source of methane and are thus important to estimating the global methane budget and evaluating its contribution to global greenhouse gas emissions. Based on the greenhouse gas inventory guidelines from the Intergovernmental Panel on Climate Change (IPCC) and the first-order decay method used to estimate emissions from MSW landfills – and in line with MSW management in various regions – we calculated methane emissions from MSW landfills in various Chinese provinces from 2003 to 2013. During this period, methane emissions from MSW landfills increased from 1141.10 Gg to 1858.98 Gg, representing a mean annual increase of 71.79 Gg. MSW emissions tended to increase more in the northern and western provinces than in the southern and eastern provinces, as methane emissions strongly and positively correlated with population and socioeconomic demographics. MSW decontamination is growing rapidly in China, and landfills predominate in all MSW treatments; moreover, incineration has also dramatically increased in recent years.

Abstract To assess the electric power grid environment under the high penetration of photovoltaic (PV) generation, it is important to construct an accurate representation of PV power output for any location in the southwestern United States at resolutions down to 10-min time steps. Existing analyses, however, typically depend on sparsely spaced measurements and often include modeled data as a basis for extrapolation. Consequentially, analysts have been confronted with inaccurate analytic outcomes due to both the quality of the modeled data and the approximations introduced when combining data with differing space/time attributes and resolutions. This study proposes an accurate methodology for 10-min PV estimation based on the self-consistent combination of data with disparate spatial and temporal characteristics. Our Type I estimation uses the nearby locations of temporally detailed PV measurements, whereas our Type II estimation goes beyond the spatial range of the measured PV incorporating alternative data set(s) for areas with no PV measurements; those alternative data sets consist of: (1) modeled PV output and secondary cloud cover information around space/time estimation points, and (2) their associated uncertainty. The Type I estimation identifies a spatial range from existing PV sites (30–40 km), which is used to estimate accurately 10-min PV output performance. Beyond that spatial range, the data-quality-control estimation (Type II) demonstrates increasing improvement over the Type I estimation that does not assimilate the uncertainty of data sources. The methodology developed herein can assist the evaluation of the impact of PV generation on the electric power grid, quantify the value of measured data, and optimize the placement of new measurement sites.

This paper presents a new security-constrained multiobjective optimal dispatch (SC-MOOD) framework for an economic and reliable operation of microgrids. The framework is developed based on a computationally effective multiobjective optimization technique, Pareto concavity elimination transformation (PaCcET). The new method considers grid steady-state and dynamic constraints while solving the optimal dispatch in both grid-connected and islanded modes. The constraints consist of power balance, voltage magnitude, line flows, power generation, frequency, and voltage transients. The proposed framework finds the most economic operating solutions to not only minimize the generation cost but also minimize the reliability cost. In this paper, the PaCcET is utilized to solve the SC-MOOD. The PaCcET uses an extraordinary transformation to first transfer all the points from a multiobjective space to a transformed objective space. It then solves a linear combination of transformed objectives using a single-objective optimizer to find all the nondominated points of the original multiobjective space. The performance of the new framework is verified using the simulation results in a microgrid with several distributed energy resources in both grid-connected and islanded modes. The results are then compared with two multiobjective optimization techniques, nondominated sorting genetic algorithm II and multiobjective particle swarm optimization, as two well-known benchmarks.

Abstract With the improvement on materials performance gradually reaching its bottleneck, more and more works focus on optimizing the device structures to seek further performance enhancement of thermoelectric (TE) devices. This paper proposed an analytical model to calculate the performance of thermoelectric generators (TEGs) with varied leg cross-sections considering temperature-dependent material properties. The explicit expressions of output power and efficiency are derived and the model is verified by finite element method (FEM). Furthermore, the optimization of leg geometry for higher device performance is performed based on the presented model, the results show that the key to maximum output power is to minimize the leg resistance; and increasing the leg volume may simultaneously raise the output power and efficiency. In addition, we apply the presented model to optimize the leg shape of a commercial TEG module and find that the optimized geometry can simultaneously achieve an enhancement up to 43.1% for output power and 9.67% for efficiency. The presented model thus may be useful in designing high-performance TEGs and shed considerable light on the principles of TEG design.

For mitigating the urgency of constructing power plant, alleviating supply pressure of power company, electricity scheduling of customers is a very important issue, wherein to promote demand response is a key factor. The demand response is mainly through electricity price publicized by utility company to guide customers in electricity scheduling, and by use of price negotiating mechanism, to reach mutual benefits for both sides of demand and supply. The paper proposes a method of minimizing tariff for customers through changing elastic load use time intervals where customers' electricity use time is divided into inelastic and elastic intervals by electricity use characteristics. In the paper, customer's one day electricity used is assumed to conduct simulation, by genetic algorithm, comparing variations among scheduling and tariff under different electricity use limitation situations. As shown in the results, it is found that through elastic load use time interval changes, minimum tariff objective can be reached, and feasibility of the proposed method is verified.

Abstract Green stormwater infrastructure is a common feature of urban cities which is mostly designed for hydrological and water quality purposes. The last decade has seen a rise in research on the environmental impact assessment of vegetated water sensitive urban design (WSUD) technologies. However, the added ecosystem benefits of these systems, such as carbon sequestration, have received less attention. In this study, the life cycle net carbon footprint of various vegetated WSUD technologies namely green roofs, rain gardens, bioretention basins, vegetated swales and stormwater ponds, have been reviewed and analysed including their carbon sequestration potential. The carbon footprint of each vegetated WSUD technology was evaluated through the four phases of the life cycle assessment (LCA): material production, construction, operation and maintenance and end-of-life phases. The results of this study show that the initial embodied carbon associated with production, transportation and construction phases is the major contributor to the carbon footprint for most of the vegetated WSUD technologies. Rain gardens are shown to provide the highest carbon sequestration potential which offsets its carbon footprint. Carbon sequestration of bioretention basins, green roofs, vegetated swales and stormwater ponds can mitigate approximately 70%, 68%, 45% and 8% of their carbon footprint respectively. This study demonstrates the significant role of carbon sequestration in mitigating the carbon footprint from the assigned life time of the vegetated WSUD technologies. The results presented in this study will allow designers and policymakers to include the carbon implication in their WSUD strategies.

Abstract Lithium-ion batteries (LIBs) have driven the industry of rechargeable batteries in recent years due to their advantages such as high energy and power density and relatively long lifespan. Nevertheless, the dispose of spent LIBs has harmful impacts on the environment which needs to be addressed by recycling LIBs. However, none of the currently developed recycling processes is economical. The physical recycling process of LIBs may be economical if the cathode active materials can be separated, regenerated, and reused to make new LIBs. However, the first barrier for regeneration and reusing is the separation of different types of spent cathode active materials in the filter cake that are mixed with each other and come in the form of very fine powders with various sizes (< 30 μm) from the physical recycling process. The aim of this study is to separate the mixture of cathode active materials by adopting Stokes’ law. The focus will be only on mechanical separation with no thermal or chemical separation methods. For the validation, an experiment was designed and successfully performed where different types of spent cathode materials (e.g., LiCoO2, LiFePO4, and LiMn2O4) were separated from the spent anode materials (e.g., graphite) with high efficiency and reasonable time.