Multi-objective optimization approach to design and planning hydrogen supply chain under uncertainty: A Portugal study case

Minimizing energy consumption by joint radio and computing resource allocation in Cloud-RAN

Global warming and mitigation potential of milk and meat production in Lombardy (Italy)

The potential environmental impacts of producing and using future electric vehicles (EVs) are important given their expected role in mitigating global climate change and local air pollutants. Recently, studies have begun assessing the effect of potential future changes in EVs supply chains on overall environmental performance. This study contributes by integrating expected changes in future energy, iron, and steel production in the life cycle assessment (LCA) of EVs. In this light, the study examines the impacts of changes in these parameters on producing and charging future EVs. Future battery electric vehicles (BEV) could have a 36–53% lower global warming potential (GWP) compared to current BEV. The change in source of electricity generation accounts for 89% of GWP reductions over the BEV’s life cycle. Thus, it presents the highest GWP reduction potential of 35–48%. The use of hydrogen for direct reduction of iron in steelmaking (HDR-I) is expected to reduce vehicle production GWP by 17% compared to current technology. By accounting for 9% of the life cycle GWP reductions, HDR-I has the second-highest reduction potential (1.3–4.8%). The results also show that the potential for energy efficiency improvement measures for GWP reduction in vehicle and battery manufacture would be more beneficial when applied now than in the distant future (2050), when the CO2 intensity of the EU electricity is expected to be lower. Interestingly, under the same conditions, the high share of renewable energy in vehicle supply chains contributed to a decrease in all air pollution-related impact categories, but an increase in toxicity-related categories, as well as land use and water consumption.

Reliability of network operation is becoming an important issue due to increase in renewable energy sources (RESs). The system controllability is degenerating, while uncertainty due to RESs is increasing and threatening the system security. We refer to the robustness of power system in this context as “Robust Power System Security.” This paper proposes a new approach to monitor the region of robust power system security taking into account the conventional N-1 security criterion against uncertainties. Forecasted RESs and demands with their confidence intervals (CIs) are specified to formulate a problem for the evaluation of the size of the worst case feasible region, where positive size implies feasibility, while negative, infeasibility. A load dispatch problem using CIs of RESs forecast is studied to evaluate the region of robust power system security. The problem is formulated as bi-level optimization, which is linearized and transformed into the mixed integer linear programming (MILP) problem. This is a new approach in the treatment of uncertainties. Simulation results show that the proposed method is a promising approach to assess the degree of feasibility of system operation and planning.

The Unmanned Aerial Vehicles (UAVs), commonly known as drones, are significant in the agriculture sphere to automate the work such as: data acquisition, crop spraying among others. This paper proposes a path planning solution for a team of UAVs that is required to whiten a greenhouse’s roof, this problem having both social-economic impact, as well as scientific-technical impact. First, the space around the roof is partitioned into cells based on a 3D cell decomposition technique, labeling the cells including the surface of the roof as regions of interest (ROIs). Based on this representation, a Petri net (PN) model captures the motion of drones, while their trajectories are returned by a Mixed Integer Linear Programming (MILP) problem which optimizes the energy consumption. An adaptable strategy is used such that the MILP problem considers only the available UAVs with enough energy to reach the ROIs. The algorithm is implemented in MATLAB and the simulation results are captured in a video link, evaluating the impact of the size of the team and the precision used for mapping the environment over the running time.

The surveillance problem is to find optimal trajectories of agents that patrol a given area as evenly as possible. In this paper, we consider multiple agents with fuel constraints. The surveillance area is given by a weighted directed graph, where the weight assigned to each arc corresponds to the fuel consumption/supply. For each node, the penalty to evaluate the unattended time is introduced. Penalties, agents, and fuels are modeled by a mixed logical dynamical system model. Then, the surveillance problem is reduced to a mixed integer linear programming (MILP) problem. Based on the policy of model predictive control, the MILP problem is solved at each discrete time. In this paper, the feasibility condition for the MILP problem is derived. Finally, the proposed method is demonstrated by a numerical example.

Allocation issues in LCA methodology: a case study of corn stover-based fuel ethanol

Vegetables are important economic crops globally, and their production has approximately doubled over the past 20 years. Globally, vegetables account for 13% of the harvested area but consume 25% of the fertilizer, leading to serious environmental impacts. However, the quantitative evaluation of vegetable production systems in subtropical plateau lake basins and the establishment of optimal management practices to further reduce environmental risks are still lacking. Using the life cycle assessment method, this study quantified the global warming, eutrophication, acidification, and energy depletion potential of vegetable production in a subtropical plateau lake basin in China based on data from 183 farmer surveys. Our results indicated that vegetable production in the study area, the Erhai Lake Basin, was high but came at a high environmental cost, mainly due to low fertilizer efficiency and high nutrient loss. Root vegetables have relatively high environmental costs due to the significant environmental impacts of fertilizer production, transportation, and application. A comprehensive analysis showed that the vegetable production in this region exhibited low economic and net ecosystem economic benefits, with ranges of 7.88-8.91 × 103 and 7.35-8.69 × 103 $ ha-1, respectively. Scenario analysis showed that adopting strategies that comprehensively consider soil, crop, and nutrient conditions for vegetable production can reduce environmental costs (with reductions in global warming potential (GWP), eutrophication potential (EP), acidification potential (AP), and energy depletion potential (EDP) by 10.6-28.2%, 65.1-73.5%, 64.5-71.9%, 47.8-70.4%, respectively) compared with the current practices of farmers. This study highlighted the importance of optimizing nutrient management in vegetable production based on farmers' practices, which can achieve more yield with less environmental impacts and thereby avoid the "trade-off" effect between productivity and environmental sustainability.

The integration of fluctuating renewable energies leads to higher price fluctuations in day-ahead markets, consequently the incentives for the activation of flexible loads increase. Even if relative forecasting errors decrease, the absolute forecasting error of renewable power production is expected to increase, therefore the demand for reserve power will rise. To fully exploit the economic potential of evolving energy markets through the utilization of production process flexibility, multiple markets need to be considered at the same time. Production planning and the participation in the reserve markets can be formulated as a multistage Stochastic Mixed-Integer Linear Programming (SMILP) problem that minimizes the expected total costs, which consists of cost for purchasing power subtracted by the revenues from offering reserve energy. The presented approach incorporates a production process model which considers uncertainties of spot and reserve market prices in terms of a stochastic process.

Multicommodity flow models are commonly used to formulate the logical topology design (LTD) problem and the lightpath routing (LR) problem as mixed integer linear programming (MILP) problems. In general, MILP formulations are intractable even for relatively small networks due to the combinatorial complexity of the problem. In this paper we propose improvements to these models and a method to solve the LTD and the LR problems in a combined manner. The interest is two fold: firstly, by tackling the two problems with separate models, problem instances of realistic size (up to 14 nodes in this paper) can be dealt with. Furthermore, different combinations of optimization models and objective functions can be investigated in a modular manner. Secondly, the mechanisms proposed to combine the problems allow to keep track of the global design problem when solving each individual step.

Process systems engineering is one of the many areas in which mixed integer optimisation formulations have been successfully applied. The nature of the problems requires specialised solution strategies and computer packages or callable libraries able to be extended and modified in order to accommodate new solution techniques. Object-oriented programming languages have been identified to offer these features. Process system applications are usually of large scale, and require modelling and solution techniques with high level of customisation. ooMILP is a library of C++ callable procedures for the definition, manipulation and solution of large, sparse mixed integer linear programming (MILP) problems without the disadvantages of many existing modelling languages. We first present a general approach to the packaging of numerical solvers as software components, derived from material developed for the CAPE-OPEN project. The presentation is in the context of construction and solution of Mixed Integer Linear Programming (MILP) problems. We then demonstrate how this package, based on the use of CORBA interfaces for synchronous execution within a single process, can be adapted with a minimum of problem-specific changes to provide a distributed solution.

The production of concrete is one of the most significant contributors to global greenhouse gas emissions. This work focuses on bio-cementation-based products and their potential to reduce global warming potential (GWP). In particular, we address a proposed bio-cementation method employing bacterial metabolism in a two-step process of limestone dissolution and recrystallisation (BioZEment). A scenario-based techno-economic analysis (TEA) is combined with a life cycle assessment (LCA), a market model and a literature review of consumers’ willingness to pay, to compute the expected reduction of global GWP. Based on the LCA, the GWP of 1 ton of BioZEment is found to be 70–83% lower than conventional concrete. In the TEA, three scenarios are investigated: brick, precast and onsite production. The results indicate that brick production may be the easiest way to implement the products, but that due to high cost, the impact on global GWP will be marginal. For precast production the expected 10% higher material cost of BioZEment only produces a marginal increase in total cost. Thus, precast production has the potential to reduce global GWP from concrete production by 0–20%. Significant technological hurdles remain before BioZEment-based products can be used in onsite construction scenarios, but in this scenario, the potential GWP reduction ranges from 1 to 26%. While the potential to reduce global GWP is substantial, significant efforts need to be made both in regard to public acceptance and production methods for this potential to be unlocked.

Knowledge representation of mathematical optimization problems and constructs for modeling

Forests can play a key role in the mitigation of climate change, although there have been limited regional scale assessments that account for variations in soil type and tree species. Most of the focus has been on their ability to sequester atmospheric CO2, while there is less information on the two other major greenhouse gases (GHGs), N2O and CH4. We examined the GHG budgets of ten forest soils in Poland, considering all three major GHGs, where no previous long-term measurements had been made, which encompassed different tree species, stand age, and contrasting edaphic conditions. In addition to the quantification and assessment of seasonal variability in the major soil GHG fluxes over two years, the aims of the present study were (i) the identification of the main drivers of the soil-based GHG fluxes, (ii) the determination of the contribution of each gas to the Global Warming Potential (GWP), and (iii) to assess the mitigation potential of these fluxes over different forest systems. All the forest soils were sources of CO2 and N2O and sinks for atmospheric CH4 with pronounced seasonal variations in CO2 and CH4 driven by soil moisture and temperature. The soils showed significant differences in annual GHG fluxes, with average values of 16.7 Mg CO2 ha−1, − 3.51 kg CH4 ha−1, and 0.95 kg N2O ha−1. The annual total GWP ranged from 13.1 to 22.0 Mg CO2 eq ha−1 with CO2 making the highest contribution, and forest-specific CH4 uptake resulting in a reduction in GWP, ranging from − 0.08% (in the youngest forest) to -0.97% (in the oldest forest). Mixed forests showed the greatest potential for climate change mitigation, with the highest soil C sequestration, and the lowest GWP values when compared to sites with monocultures. The results suggest that a mixture of tree species could eventually be incorporated into management plans to increase the effectiveness of forests in climate change mitigation.

The energy sector has dominated the world attention as recently arrived shale gas and slumping crude oil prices globally affect Petroleum Supply Chains (PSC). Fluctuating demand and prices are forcing companies to rethink existing distribution networks and production plans in order to improve network flexibility and cost efficiency. The current paper addresses this problem and presents a stochastic mixed integer linear program (MILP) for PSC design and planning under demand uncertainty that maximizes the expected net present value (ENPV) of a multi-entity multi-product PSC network. The decisions include locations, capacities, transport modes, routes, product affectations, inventories and fair-price costs and tariffs with economies of scale. The stochastic MILP designs a grassroots network with demand uncertainty for the Portuguese PSC, drawing important insights and research directions.