• Energy Research
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Background: The global palm oil market experienced a remarkable boom since the year 2000. Since palm oil can be used for biodiesel production, the global expansion of oil palm cultivation has been associated with the global biofuel boom. Biofuel policies—especially those adopted in the European Union (EU)—have been blamed for the socio-environmental impacts of oil palm expansion. We explore how the global biofuel boom interacts with national geographies and social-economic and political processes to produce country-specific trajectories of biofuel crops expansion. We analyse the expansion of oil palm cultivation in Colombia between 2000 and 2010 from a political ecology perspective. Methods: The analysis is based on a framework that positions expansion of commodity frontiers within the ‘space-of-flows’ and the ‘space-of-place’. Through this approach, we identify the markets and geographies that define the country-specific trajectories of expansion of oil palm in Colombia, and their connections with general patterns of land control. The empirical analysis is based on primary data collected during fieldwork, and on an extensive review of secondary data about the palm oil sector and the socio-environmental effects of oil palm expansion in the country. Results: The contemporary oil palm expansion in Colombia was not specifically influenced by the international biofuel market. Expansion was characterized by an increasing production of palm oil for biodiesel, to supply a policy-driven national biofuel market controlled by national palm oil producers. The evidence shows that this oil palm expansion proceeded through a variety of land control practices that constitute forms of ‘accumulation by dispossession’ and ‘assimilation’. These are embedded in contextual factors that include the agrarian history of Colombia, the armed conflict, and government policies. Conclusions: Our study shows that the ways in which expansion of biofuel crops unfold in each producing country depend not only on the global biofuel market. They are also shaped by the country-specific geographies and political economies. Therefore, research and policies on the global expansion of energy crops should account for the complex and interrelated factors that mediate the specific ways in which the global demand for biofuels creates biofuel crop booms at country level.

This study focuses on formulating the most sustainable concrete by incorporating recycled concrete aggregates and other products retrieved from construction and demolition (C&D) activities. Both recycled coarse aggregates (RCA) and recycled fine aggregates (RFA) are firstly used to fully replace the natural coarse and fine aggregates in the concrete mix design. Later, the cement rich ultrafine particles, recycled glass powder and mineral fibres recovered from construction and demolition wastes (CDW) are further incorporated at a smaller rate either as cement substituent or as supplementary additives. Remarkable properties are noticed when the RCA (4–12 mm) and RFA (0.25–4 mm) are fully used to replace the natural aggregates in a new concrete mix. The addition of recycled cement rich ultrafines (RCU), Recycled glass ultrafines (RGU) and recycled mineral fibres (RMF) into recycled concrete improves the modulus of elasticity. The final concrete, which comprises more than 75% (wt.) of recycled components/materials, is believed to be the most sustainable and green concrete mix. Mechanical properties and durability of this concrete have been studied and found to be within acceptable limits, indicating the potential of recycled aggregates and other CDW components in shaping sustainable and circular construction practices. The authors wish to acknowledge the financial support from EU Horizon 2020 Project VEEP ‘‘Cost-Effective Recycling of C&DW in High Added Value Energy Efficient Prefabricated Concrete Compo-nents for Massive Retrofitting of our Built Environment” (No.723582).

Short-term generation scheduling is an important function in daily operational planning of power systems. It is defined as optimal scheduling of power generators over a scheduling period while respecting various generator constraints and system constraints. Objective of the problem includes costs associated with energy production, start-up cost and shut-down cost along with profits. The resulting problem is a large scale nonlinear mixed-integer optimization problem for which there is no exact solution technique available. The solution to the problem can be obtained only by complete enumeration, often at the cost of a prohibitively computation time requirement for realistic power systems. This paper presents a hybrid algorithm which combines Lagrangian Relaxation (LR) together with Evolutionary Algorithm (EA) to solve the problem in cooperative and competitive energy environments. Simulation studies were carried out on different systems containing various numbers of units. The outcomes from different algorithms are compared with that from the proposed hybrid algorithm and the advantages of the proposed algorithm are briefly discussed.

Current energy market designs and pricing schemes fail to give investors the appropriate market signals. In particular, energy prices are not high enough to attract investors to build new or maintain existing power capacity. In this paper we propose a method to compute second-best Pareto optimal equilibrium prices for any market exhibiting non-convexities and, based on this result, an energy market design able to restore the correct energy price signals for supply investors.

Direct Load Control (DLC) is an effective instrument for achieving a guaranteed load curtailment. Unfortunately, if a customer considers that personal discomfort outweighs money savings after DLC shutting down of home appliances, DLC solution can be rejected. This paper proposes the way to remediate customer comfort concerns by pre-storing additional energy in electric loads before their disconnection from the grid. We take electric tank water heaters as an illustrative example of residential loads with storage. To illuminate our approach we first show how to balance electric consumption for pre-storing with user thermal discomfort for a single water heater. Then, we illustrate how the approach can be scaled-up to a multiple-boiler scenario, when ten remotely controlled boilers act next to fifty non-controlled boilers. The simulations for the latter case show that the expected user thermal discomfort can be significantly reduced at the cost of a reasonable increase of electricity demand preceding DLC event.

Microgrids are increasingly being used as a platform to integrate distributed generation such as renewable energy sources and (RESs) conventional sources in both grid-connected and isolated power systems. Due to the inherent intermittent nature of RESs, energy storage systems (ESSs) that can absorb fluctuations have become inevitable. Nevertheless, large capacities of ESSs increase the initial cost while small capacities lead to instabilities and increase in the cost of conventional fuels. Therefore, finding the optimal size of the ESS for a given application is essential for the reliable, efficient and economical operation of a microgrid. Once the battery size is decided, maintaining its energy at appropriate levels is essential to ensure stable and safe operation of the microgrid. This paper presents a novel expert fuzzy system - grey wolf optimization (FL-GWO) based intelligent meta-heuristic method for battery sizing and energy management. The proposed energy management operation is carried out by a Grey Wolf Optimiser (GWO) that is helped to set the membership functions and rules of the fuzzy logic expert system. The unit commitment (UC) issue, which is essential for the proper operation of the isolated microgrid, has been additionally considered in this paper. To verify the performance of the proposed method, results are compared with the rules-based method and traditional GWO algorithm. It has been proven from the results that the FL-GWO has a significant convergence property and capability to minimize the Levelized Cost Of Electricity (LCOE) by 14.13% and 24.15% compared with conventional GWO algorithm and rules-based method, respectively. The weather conditions for different climates is used to verify the performance of the intelligent energy management method under different operating scenarios. The results show that the intelligent online multi-objective energy management strategy is capable of managing a smooth power flow with the same optimal configuration in the isolated microgrid, minimising the fossil fuel utilisation and reducing the CO2 emission level.

Abstract Global hydropower growth continues to accelerate with 25% of total capacity installed in just the last 10 years. This accelerating expansion and the important storage facility hydropower means it is increasingly important to understand the reasons for operational failures. This is a challenge because the major reason for failures involves the complex interaction of hydraulic, mechanical and electric subsystems. Historically, reliability modelling has been split in two directions, focusing on different sub-systems, and has not yet been unified. Here these approaches are unified with a novel expression of unbalanced forces. This model with operational data are validated and the important modes of oscillation in the shaft are identified. Finally, the mechanism of the first-order oscillation mode exciting a second-order mode is presented. This integrated and accurate mathematical model is a major advance in the diagnosis and prediction of failures in hydropower operation.

The unpredictable nature of wind energy makes its integration to the electric grid highly challenging. However, these challenges can be addressed by incorporating storage devices (batteries) in the system. We perform an overall assessment of a single domestic power system with a wind turbine supported by an energy storage device. The aim is to investigate the best operation mode of the storage device such that the occurrence of large power spills can be minimized. For estimating the small probability of large power spills, we use the splitting technique for rare-event simulations. An appropriate Importance Function for splitting is formulated such that it reduces the work-load of the probability estimator as compared to the conventional Crude Monte Carlo probability estimator. Simulation results show that the ramp constraints imposed on the charging/discharging rate of the storage device plays a pivotal role in mitigating large power spills. It is observed that by employing a new charging strategy for the storage device large power spills can be minimized further. There exists a trade-off between reducing the large power spills versus reducing the average power spills.