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This paper provides an analysis of challenges and available solutions for exterior insulated panels suitable for deep energy retrofits of existing building envelopes. The analysis covers a review of available technologies that provide flexible retrofit insulated panels suitable for multiple climates and building typologies. Moreover, the paper proposes a new design for insulated retrofit panels that account for the majority of identified technical risks including cost, architectural diversity, climate variations, structural concerns, moisture resilience, air sealing, and water sealing. Additionally, the proposed design can be easily installed with minimal disruption to the occupants. A series of parametric and optimization analyses is carried out to identify the optimal design specifications for insulated panels suitable for deep retrofits of existing US housing stocks. The analysis results show that the optimal design criteria for the insulated panels can reduce heating and cooling energy consumption by up to 80% and HVAC capacities by 70%. Moreover, the results indicate that these insulated panels are highly cost effective for retrofitting US housing units located in cold climates.

Meeting Germany’s climate targets urgently demands substantial investment in renewable energies such as hydrogen, as well as tackling industrial CO2 emissions with a strong CO2 transport infrastructure. This is particularly crucial for CO2-heavy industries such as steel, cement, lime production, power plants, and chemical plants, given Germany’s ban on onshore storage. The CO2 transport network is essential for maintaining a circular economy by capturing, transporting, and either storing or utilizing CO2. This study fills gaps in CO2 pipeline transport research, examining pipeline diameters, costs, and pressure drop, and providing sensitivity analysis. Key findings show that the levelized cost of CO2 transport (LCO2T) ranges from 0.25 €/t to 55.82 €/t based on varying transport masses (1000 t/day to 25,000 t/day) and distances (25 km to 500 km), with compression costs pushing LCO2T to 33.21 €/t to 92.82 €/t. Analyzing eight pipeline diameters (150 mm to 500 mm) and the impact of CO2 flow temperature on pressure loss highlights the importance of selecting optimal pipeline sizes. Precise booster station placement is also crucial, as it significantly affects the total LCO2T. Exploring these areas can offer a more thorough understanding of the best strategies for developing cost-effective, efficient, and sustainable transport infrastructure.

Geographically distributed cloud data centers (DCs) consume enormous amounts of energy to meet the ever-increasing processing and storage demands of users. The brown energy generated using fossil fuels is expensive and significantly contributes to global warming. Considering the environmental impact caused by the high carbon emissions and relatively high energy cost of brown energy, we propose the integration of renewable energy sources (RES), especially solar and wind energy, with brown energy to power cloud data centers. In our earlier study, we addressed the intermittency of renewable energy sources, where we replaced the random initialization of artificial neural network (ANN) edge weights with the harmony search algorithm (HSA)-optimized assignment of weights. This study incorporated reliably forecast solar and wind energy into the input parameters of our proposed green energy manager (GEM), for cost minimization, carbon emission minimization, and better energy management of cloud DCs, to make our current study more reliable and trustworthy. Four power sources, on-site solar energy and wind energy, off-site solar energy and wind energy, energy stored in energy storage devices, and brown energy, were considered in this study and simulations were carried out for three different cases. The simulation results showed that case 1 (all brown) was 58% more expensive and caused 71% higher carbon emissions than case 2.1 (cost minimization). Case 1 (all brown) was 39% more expensive and had 80% higher carbon emissions than case 2.2 (carbon emission minimization). The simulation results justify the necessity and importance of the GEM, and finally the results proved that our proposed GEM is less expensive and more environmentally friendly.

The increased decentralization of renewable energy has increased curtailment rates in stagnating demand zones, increasing the levelized cost of energy (LCOE). The geographically dynamic nature of gravity energy storage (GES) is emerging in the field of mechanical energy storage, over pumped hydro. However, GES costs vary geospatially, specifically in decentralized suburban areas, due to the impact of urban socioeconomics. This study aims to find a mathematical approximation of a cost-optimized location for suburban Solar–GES hybrid systems in curtailment-prone areas. A multi-parameterization model mathematically programmed land, transmission, supply chain and excavation costs into geospatial matrix approximations for suburban areas of 2500 km2 in Fukuoka and Ibaraki in Japan. It was found that SPV-GES location-dependent costs were mainly affected by distance from the city’s economic center and flat plains in suburbs, and supply chain and transmission costs optimized the location-dependent cost for GES at a specific point. It was also found that flat terrains were more economical than mountainous terrains due to high GES supply chain costs. With GES found to be cost-competitive compared to other storage technologies in Japan, this study reveals that GES introduction benefits the LCOE of suburban, decentralized SPV when curtailment is >50% irrespective of terrain.

In recent years, with the growing number of EV charging stations integrated into the grid, optimizing the aggregated EV load based on individual EV flexibility has drawn aggregators’ attention as a way to regulate the grid and provide grid services, such as day-ahead (DA) demand responses. Due to the forecast uncertainty of EV charging timings and charging energy demands, the actual delivered demand response is usually different from the DA bidding capacity, making it difficult for aggregators to profit from the energy market. This paper presents a two-layer online feedback control algorithm that exploits the EV flexibility with controlled EV charging timings and energy demands. Firstly, the offline model optimizes the EV dispatch considering demand charge management and energy market participation, and secondly, model predictive control is used in the online feedback model, which exploits the aggregated EV flexibility region by reducing the charging energy based on the pre-decided service level for demand response in real time (RT). The proposed algorithm is tested with one year of data for 51 EVs at a workplace charging site. The results show that with a 20% service level reduction in December 2022, the aggregated EV flexibility can be used to compensate for the cost of EV forecast errors and benefit from day-ahead energy market participation by USD 217. The proposed algorithm is proven to be economically practical and profitable.

A series of distributed flexural cracks develop in reinforced concrete flexural elements under the working load. The control of cracking in reinforced concrete is an important issue that must be considered in the design of reinforced concrete structures. Crack width and spacing are influenced by several factors, including the steel percentage, its distribution in the concrete cross-section, the concrete cover, and the concrete properties. In practice, however, a compromise must be made between cracking, durability, and ease of construction and cost. This study presents the optimal design of a reinforced concrete cross-section, using the optimization method of mixed-integer nonlinear programming (MINLP) and the Eurocode standard. The MINLP optimization model OPTCON was developed for this purpose. The model contains the objective function of the material cost considering the crack width requirements. The crack width requirements can be satisfied by direct calculation or by limiting the bar spacing. Due to the different crack width requirements, two different economic designs of reinforced concrete sections were proposed. The case study presented in this study demonstrates the value of the presented optimization approach. A direct comparison between different methods for modelling cracking in reinforced concrete cross-sections, which has not been done before, is also presented.

In small apparel manufacturing, unit price determination is often based on production duration given by customers and design complexity rather than information relating to internal labor resources. However, labor expertise and skills are critical factors that outweigh the machinery and technology in small and medium apparel companies. The quality of the product greatly depends on the experience and delicacy of the tailors. Using data on labor skill and wage levels in the planning process will benefit human resource utilization, increasing productivity, and profits effectively. This paper proposes a general mathematical model for task allocation and cost optimization for small and medium apparel companies. The model handles task allocation and cost minimization problems that must ensure processing time requirements and balance workloads for operators. The developed model tests two case studies in a published paper. The results prove that although the proposed model is simple, it has high applicability and efficiency in solving allocation optimization problems. The authors then integrate the formulations into a Standalone desktop app in the MATLAB “App designer” module. With a standalone desktop app, end users can enjoy the application. This app has a user-friendly design. Users unfamiliar with computers or planners with no background in programming can use the app to tackle similar optimization problems. The proposed mathematical model can further expand to include more complex issues in apparel companies and can also be a good reference for other fields.

In order to encourage energy saving and the adoption of renewable sources, this study provides a comprehensive experimental framework that integrates socioeconomic and behavioral objectives for the local energy community. The experiment aims to find out how successfully using behavioral interventions might encourage customers to save electrical energy and encourage them to adopt renewable energy, e.g., solar photovoltaic energy, in the present case. Using this method, we can calculate the causal impact of the intervention on consumer participation in the local electricity sector. The study uses consumer data on the import and export of electrical power from retailer electricity utilities at a predetermined power exchange price and a midmarket price for local energy community power transactions. The local energy community model simulates the consumption, storage, and export of 20 residential customers who, in different scenarios, are the test subjects of an empirical experiment and embrace electricity conservation and renewable energy. We address the optimization issue of calculating the power exchange cost and revenue in various scenarios and comparing them with the base case cost. The cases are built on the customers’ behavioral interventions’ empirical response. The findings demonstrate that the interaction of socioeconomic and behavioral objectives leads to impressive cost savings of up to 19.26% for energy utility customers. The policy implication is suggested for local energy utilities.

Generally, greenhouses are high energy-consuming, sometimes accounting for 50% of the cost of greenhouse production. Geothermal energy plays a very important role in maintaining the desired temperature and reducing energy consumption. This work deals with a project of a hybrid heating plant (97% geothermal energy and 3% gas-condensing boiler) for the innovative Plant Phenotyping Greenhouse at the University Campus in Grugliasco (few km West of the city of Turin). The aim of the study is to testify to the energy efficiency of this kind of hybrid plant as well as its economic sustainability. Numerical simulations of a GRT were used to calibrate the system and verify that the software reasonably modeled the real case. They helped to correctly size the geothermal plant, also providing data about the thermal energy storage and production during on and off plant cycles. The results show a thermal power of 50.92 kW over 120 days of plant operation, in line with the expected energy needs to meet the base load demand. Long-term results further ensure a negligeable impact on the ground, with a thermal plume between 5 and 10 m from the plant, reducing substantially in a few months after switching off the plant.

The Measurement-Device-Independent-Quantum Key Distribution (MDI-QKD) has the advantage of extending the secure transmission distances. The MDI-QKD combined with the Hybrid-Trusted and Untrusted Relay (HTUR) is used to deploy large-scale QKD networks, which effectively saves deployment cost. We propose an improved scheme for the QKD network architecture and cost analysis, which simplifies the number of QKD transmitters and incorporates the quantum key pool (QKP) in the QKD network. We developed a novel Hybrid-QKD-Network-Cost (HQNC) heuristic algorithm to solve the cost optimization problem. Simulations verified that the scheme in this paper could save the cost by over 50 percent and 90 percent, respectively.

In today’s business environment, reducing costs is crucial due to the variety of Internet of Things (IoT) devices and security infrastructure. However, applying security measures to complex business scenarios can lead to performance degradation, making it a challenging task. To overcome this problem, we propose a novel algorithm based on deep reinforcement learning (DRL) for optimizing cost in multi-party computation software-defined security middle platforms (MPC-SDSmp) in real-time. To accomplish this, we first integrate fragmented security requirements and infrastructure into the MPC-SDSmp cloud model with privacy protection capabilities to reduce deployment costs. By leveraging the power of DRL and cloud computing technology, we enhance the real-time matching and dynamic adaptation capabilities of the security middle platform (Smp). This enables us to generate a real-time scheduling strategy for Smp resources that meet low-cost goals to reduce operating costs. Our experimental results demonstrate that the proposed method not only reduces the costs by 13.6% but also ensures load balancing, improves the quality-of-service (QoS) satisfaction by 18.7%, and reduces the average response time by 34.2%. Moreover, our solution is highly robust and better suited for real-time environments compared to the existing methods.

With the advances in the IoT era, the number of wireless sensor devices has been growing rapidly. This increasing number gives rise to more complex networks where more complex tasks can be executed by utilizing more computational resources from the public clouds. Cloud service providers use various pricing models for their offered services. Some models are appropriate for the cloud service user’s short-term requirements whereas the other models are appropriate for the long-term requirements of cloud service users. Reservation-based price models are suitable for long-term requirements of cloud service users. We used the pricing schemes with spot and reserved instances. Reserved instances support a hybrid cost model with fixed reservation costs that vary with contract duration and an hourly usage charge which is lower than the charge of the spot instances. Optimizing resources to be reserved requires sufficient research effort. Recent algorithms proposed for this problem are generally based on integer programming problems, so they do not have polynomial time complexity. In this work, heuristic-based polynomial time policies are proposed for this problem. It is exhibited that the cost for the cloud service user which uses our approach is comparable to optimal solutions, i.e., it is near-optimal.

Predictive maintenance can be efficiently improved by studying the sensitivity of the maintenance decisions with respect to changes in the proposed model parameters (costs, duration of reparation, etc.). To address this issue, we first propose an original approach that includes both maintenance costs and maintenance risks in the same objective function to minimize. This approach uses the RUL as an indicator of the health state of the system and supposes that the system is under regular inspections and can only be replaced by a new system in case of serious deterioration or failure. Then, we present a process of human decision making under uncertainty based on several criteria. Finally, we study and analyze the influence of the model parameters and their implications on the obtained maintenance policies. The study will lead to some recommendations that can improve the predictive maintenance decisions and help experts better handle maintenance costs.

Modular multilevel converter-based battery energy storage systems (MMC-based BESS) can play an important role when applied to power systems, for example, stabilizing and improving power quality. The integration of batteries in an MMC is usually performed in two ways: single-stage (SS) and two-stage (TS) (i.e., with dc/dc converter). Different references discuss the control strategies, sizing methodologies, and the advantages/drawbacks of these approaches. However, a deep comparison of these topologies is still missing in the literature. Thus, benchmarking SS and TS approaches is provided in this work. The battery current spectrum, the battery lifetime, the converter power losses, and the total costs are evaluated for both approaches. In addition, energy oversizing due to rounding is an important figure of merit since batteries account for a large amount of the costs. The case study is evaluated considering commercial battery racks (standard solution) and battery cells (customized solution). For the case studies, different insulated gate bipolar transistor (IGBT) models and states of charge (SOC) ranges are considered. The system under review is a 10.9 MVA/5.76 MWh connected to a 13.8 kV power system. This system aims to perform a time-shift for an industry. In an analysis to optimize the costs of a project that evaluates several variables, the best configuration option is found in the most balanced option. In this sense, when balancing costs in project sizing, power losses, and battery replacement, the optimal design is the SS approach in the customized solution.

In the EU countries, almost 50% of the produced energy is used in residential buildings. More than 25% of this energy is used to produce domestic hot water, of which almost 80% is used to heat water in domestic hot water circulation systems. This is due to high expectations on the part of residents based on their comfort, in particular regarding the supply of heat for heating and domestic hot water. In the course of their long-term research conducted on real systems, the authors confirmed that the operation of domestic hot water distribution systems causes significant costs, mainly due to heat losses. Therefore, typical variants of energy optimization of such systems were analyzed. Tests have shown that selected solutions, such as the use of control automation, are not sufficient, and recommended additional thermal insulation may not be applicable due to technical reasons. With an aim of finding a solution to the problem, the publication analyzes operational data from an existing heat source and domestic hot water circulation system in a residential building. On the basis of these analyses, a solution was proposed to reduce energy consumption within the installation by means of its hydraulic optimization. The reduction of heat losses in domestic hot water installation by means of a method presented by the authors is estimated at approximately 20%.

This study investigates the optimization of the design of timber floor joists, taking into account the self-manufacturing costs and the discrete sizes of the structure. This non-linear and discrete class of optimization problem was solved with the multi-parametric mixed-integer non-linear programming (MINLP). An MINLP optimization model was developed. In the model, an accurate objective function of the material and labor costs of the structure was subjected to design, strength, vibration and deflection (in)equality constraints, defined according to Eurocode regulations. The optimal design of timber floor joists was investigated for different floor systems, different materials (sawn wood and glulam), different load sharing systems, different vertical imposed loads, different spans, and different alternatives of discrete cross-sections. For the above parameters, 380 individual MINLP optimizations were performed. Based on the results obtained, a recommended optimal design for timber floor joists was developed. Engineers can select from the recommendations the optimal design system for a given imposed load and span of the structure. Economically suitable spans for timber floor joists structures were found. The current knowledge of competitive spans for timber floor joists is extended based on cost optimization and Eurocode standards.

In smart grids, a hybrid renewable energy system that combines multiple renewable energy sources (RESs) with storage and backup systems can provide the most cost-effective and stable energy supply. However, one of the most pressing issues addressed by recent research is how best to design the components of hybrid renewable energy systems to meet all load requirements at the lowest possible cost and with the best level of reliability. Due to the difficulty of optimizing hybrid renewable energy systems, it is critical to find an efficient optimization method that provides a reliable solution. Therefore, in this study, power transmission between microgrids is optimized to minimize the cost for the overall system and for each microgrid. For this purpose, artificial bee colony (ABC) is used as an optimization algorithm that aims to minimize the cost and power transmission from outside the microgrid. The ABC algorithm outperforms other population-based algorithms, with the added advantage of requiring fewer control parameters. The ABC algorithm also features good resilience, fast convergence, and great versatility. In this study, several experiments were conducted to show the productivity of the proposed ABC-based approach. The simulation results show that the proposed method is an effective optimization approach because it can achieve the global optimum in a very simple and computationally efficient way.

As the electrification of the transportation sector advances, fleet operators have to rethink their approach regarding fleet management against the background of limiting factors, such as a reduced range or extended recharging times. Charging infrastructure plays a critical role, and it is worthwhile to consider its planning as an integral part for the long-term operation of an electric vehicle fleet. In the category of fixed route transportation systems, the predictable character of the routes can be exploited when planning charging infrastructure. After a prior categorization of stakeholders and their respective optimization objectives in the sector coupling domain, a cost optimization framework for fixed route transportation systems is presented as the main contribution of this work. We confirm previous literature in that there is no one-fits-all optimization method for this kind of problem. The method is tested on seven scenarios for the public transport operator of Darmstadt, Germany. The core optimization is formulated as a mixed integer linear programming (MILP) problem. All scenarios are terminated by the criterion of a maximum solving time of 48 h and provide feasible solutions with a relative MIP-gap between 7 and 24%.

This paper aims to evaluate the life cycle greenhouse gas (GHG) emissions of importing electrical power into Singapore, generated from a large-scale solar photovoltaic (PV) power plant in Australia, through a long-distance subsea high-voltage direct current (HVDC) cable. A cost optimization model was developed to estimate the capacities of the system components. A comprehensive life cycle assessment model was built to estimate emissions of manufacturing and use of these components. Our evaluation shows that, for covering one fifth of Singapore’s electrical energy needs, a system with an installed capacity of 13GWPV, 17 GWh battery storage and 3.2GW subsea cable is required. The life cycle GHG emissions of such a system are estimated to be 110gCO2eq/kWh, with the majority coming from the manufacturing of solar PV panels. Cable manufacturing does not contribute largely toward GHG emissions. By varying full-load hours and cable lengths, it was assessed that sites closer to Singapore might provide the same energy at same/lower carbon footprint and reduced cost, despite the lower insolation as compared to Australia. However, these sites could cause greater emissions from land use changes than the deserts of Australia, offsetting the advantages of a shorter HVDC cable.

Regarding different challenges, such as integration of green energy and autonomy of microgrid (MG) in the multi-microgrid (MMG) system, this paper presents an optimized and coordinated strategy for energy management of MMG systems that consider multiple scenarios of MGs. The proposed strategy operates at two optimization levels: local and global. At an MG level, each energy management system satisfies its local demand by utilizing all available resources via local optimization, and only sends surplus/deficit energy data signals to MMG level, which enhances customer privacy. Thereafter, at an MMG level, a central energy management system performs global optimization and selects optimized options from the available resources, which include charging/discharging energy to/from the community battery energy storage system, selling/buying power to/from other MGs, and trading with the grid. Two types of loads are considered in this model: sensitive and non-sensitive. The algorithm tries to make the system reliable by avoiding utmost load curtailment and prefers to shed non-sensitive loads over sensitive loads in the case of load shedding. To verify the robustness of the proposed scheme, several test cases are generated by Monte Carlo Simulations and simulated on the IEEE 33-bus distribution system. The results show the effectiveness of the proposed model.

To be able to fulfil the Paris Climate Agreement and keep global warming with reasonable confidence at a maximum of 1.5 °C above pre-industrial levels, Germany must set an end to all greenhouse gas emissions by 2030. At the core of this task is the switch to 100% renewables across all sectors on the same time horizon. Conventional technologies fueled by fossil and nuclear energies are, according to the vast majority of current cost calculations, energetically inefficient, too expensive, and too slow in expansion to be able to deliver a substantial contribution to rapid climate protection. We present the first comprehensive energy scenario that shows the way to 100% renewable energy for all energy sectors by 2030. The result of the calculations is a cost-effective energy system that is compatible with the German share of necessary greenhouse gas reduction. This study shows a target system of generation, conversion, and storage technologies that can achieve the transformation to 100% renewable energy in all energy sectors—electricity, heat, and mobility—in time and at competitive costs below the costs of the current system. Moreover, we demonstrate the huge cost effect that arises if southern Germany renounces its onshore wind resources and find that this would substantially increase the need for high-voltage direct-current transmission capacity.

The adoption of electric buses in public transport requires careful planning for the bus fleet and charging infrastructure. A mathematical model of an urban bus service was developed to support the deployment of charging infrastructure. The novelty of the model is that it incorporates infrastructure elements for both static and dynamic charging technologies at the same time. The model supports the electrification of the bus lines without route and schedule adjustments. The volume of charged energy at charging units is considered as a variable in the objective function to determine the location of charging units at minimum cost. The model was verified by a case study based on actual bus service data. It was found that the use of static chargers is more favorable if the cost of a static charging unit is less than the cost of a dynamic charger with a length of 1600 m and the charging power of static chargers is three times greater than the charging power of dynamic chargers. The relationship between charging power and the length of the dynamic charging unit was analyzed. It was noted that the use of charging power higher than 162.5 kW at dynamic charging units is not necessary.

Green hydrogen for mobility represents an alternative to conventional fuel to decarbonize the transportation sector. Nevertheless, the thermodynamic properties make the transport and the storage of this energy carrier at standard conditions inefficient. Therefore, this study deploys a georeferenced optimal transport infrastructure for four base case scenarios in France and Germany that differs by production distribution based on wind power potential and demand capacities for the mobility sector at different penetration shares for 2030 and 2050. The restrained transport network to the road infrastructure allows focusing on the optimum combination of trucks operating at different states of aggregations and storage technologies and its impact on the annual cost and hydrogen flow using linear programming. Furthermore, four other scenarios with production cost investigate the impact of upstream supply chain cost, and eight scenarios with daily transport and storage optimization analyse the modeling method sensitivity. The results show that compressed hydrogen gas at a high presser level around 500 bar was, on average, a better option. However, at an early stage of hydrogen fuel penetration, substituting compressed gas at low to medium pressure levels by liquid organic hydrogen carrier minimizes the transport and storage costs. Finally, in France, hydrogen production matches population distribution, in contrast to Germany, which suffers from supply and demand disparity.

Analysis of the current knowledge has revealed the lack of a method for increasing the cost-effectiveness of wastewater and septage treatment in plants overloaded by contamination. This was the premise for undertaking research on the process of septage pre-treatment in a subsurface vertical flow constructed wetland (SS-VF) prior to its input into the biological section of a municipal treatment plant. In previous research the authors have indicated that this allows for a significant reduction in the value of pollution indicators. The objective of this paper is to assess the cost-effectiveness of this process by means of an optimization model. The decision variable was the coefficient of septage stream distribution into the quantity directed to the SS-VF bed in relation to its total quantity. The optimization criterion was the minimization of the expected annual cost of wastewater and septage treatment. Verification of the model has shown that it is reasonable to subject all septage to the pre-treatment in a SS-VF bed for small wastewater treatment plant (WWTPs) located in rural areas. The bigger the septage pollution load is, the greater the reduction in the treatment costs. The proposed solution is less cost-effective in urban areas, where the construction of a SS-VF bed requires land purchase and additional costs of its adjustment. Optimization results largely depend on the cost function, so it is important to build it on reliable local data.

The cost-effectiveness of energy efficiency measures meant to achieve a zero-emission office building is investigated and compared to business as usual energy efficiency measures. The laboratory for zero emission buildings, the ZEB Lab, located in Trondheim, Norway, is an office building designed and built to compensate its lifecycle emissions with the use of a large array of building-integrated photovoltaic panels, pursuing a zero-emissions ambition level. Three design alternatives are investigated by downgrading the building insulation level to the values recommended by the currently enforced Norwegian building code, the byggteknisk forskrift TEK17. A sensitivity analysis of the variation of the installed area of the photovoltaic panels is performed to evaluate if smaller areas give better cost performances. Net present values are calculated by using three scenarios of future increase of electricity price for a time horizon of 20 years. Results show that business as usual solutions give higher net present values. Optimized areas of the photovoltaic panels further increase the net present values of the business as usual solutions in the highest electricity price scenario. The zero-emission ambition level shows a higher net present value than that of the business as usual solutions for a time horizon of at least 36 years.

Quantifying the benefits of construction supply chain management through prescriptive models is a challenging and fast-growing research area that still lacks standardized optimization models with full integrative potential. In response to the needs and the peculiarities of the construction industry, this paper proposes an innovative model that merges temporal and project-based supply chains into a sustainable network with repetitive flows, large scope contracts, strategic alliances and economies of scale. It is a dynamic mixed-integer linear programming model for cost minimization of a three-echelon supply chain serving multiple sites with multiple products over a time horizon. Its novelty lies in yielding optimal decisions on network design, product quantities to be purchased and transported, shipments and inventory levels in all echelons under any logistics system in a multi-period, multi-product and multi-project environment with discount schemes and strategic preferences. The model is general enough to be implemented by any general contractor acting as a system integrator but also allows customization with logical constraints. All these features constitute an innovative, versatile and flexible managerial decision making tool. Model implementation is based on a spreadsheet optimization software and is followed by post-solution analysis, sensitivity analysis and multiple parameterized optimizations.

With the progressive addition of microgrids at the distribution level, complex networks of interconnected microgrids and the utility grid are likely to emerge. In such a scenario, advanced microgrid controllers are required to achieve operational stability objectives while maintaining a cost-effective operation. This paper investigates the control strategies, trading mechanisms, and interconnection configurations of a multi-microgrid and utility grid system for frequency stability analysis and operational cost optimization. The analysis is performed on a model of two interconnected microgrids and the utility grid, all possible interconnection configurations are tested. A robust controller is designed and the control parameters are later optimized to ensure that the frequency stability of the system is maintained under normal operating conditions and under various disturbances. A new control element based on switching between interconnection configurations is introduced to handle the power that flows between microgrids, aiming to minimize inter-microgrid energy trading cost while maintaining the system frequency fluctuations within tolerance levels. The effectiveness of the designed controller is demonstrated in this work. This work is expected to provide new insights in the field of multi-microgrid system design.

In the context of sustainable buildings, this paper investigates power flow management for an isolated DC microgrid and focuses on efficiency and energy cost reduction by optimal scheduling. Aiming at high efficiency, the local produced power has to be used where, when, and how it is generated. Thus, based on photovoltaic sources, storage, and a biofuel generator, the proposed DC microgrid is coupled with the DC distribution network of the building. The DC bus distribution maximizes the efficiency of the overall production-consumption system by avoiding some energy conversion losses and absence of reactive power. The isolated DC microgrid aims to minimize the total energy cost and thus, based on forecasting data, a cost function is formulated. Using a mixed integer linear programming optimization, the optimal power flow scheduling is obtained which leads to an optimization-based strategy for real-time power balancing. Three experimental tests, operated under different meteorological conditions, validate the feasibility of the proposed control and demonstrate the problem formulation of minimizing total energy cost.

Fossil fuels comprise 93% of Malaysia’s electricity generation and account for 36% of the country’s 2010 Greenhouse Gas (GHG) emissions. The government has targeted the installation of 330 MW of biomass electricity generation capacity by 2015 to avoid 1.3 Mt of CO2 emissions annually and offset some emissions due to increased coal use. One biomass option is to co-fire with coal, which can result in reduced GHG emissions, coal use, and costs of electricity. A linear optimization cost model was developed using seven types of biomass residues for Peninsular Malaysia. Results suggest that about 12 Mt/year of residues are available annually, of which oil-palm residues contribute 77%, and rice and logging residues comprise 17%. While minimizing the cost of biomass and biomass residue transport, co-firing at four existing coal plants in Peninsular Malaysia could meet the 330 MW biomass electricity target and reduce costs by about $24 million per year compared to coal use alone and reduces GHG emissions by 1.9 Mt of CO2. Maximizing emissions reduction for biomass co-firing results in 17 Mt of CO2 reductions at a cost of $23/t of CO2 reduced.

Based on a cost analysis, a method of identifying and predicting optimum replaced grinding wheel diameter (De.op) in a surface grinding operation for 9CrSi steel material was developed in this study. The De.op value was determined by minimizing the cost function. An experimental design was set up, and a computational program was developed to perform the experiment in order to calculate the De.op value. Furthermore, the impact of the grinding process parameters such as the initial grinding wheel diameter, the grinding wheel width, the total dressing depth, the Rockwell hardness of the workpiece, the radial grinding wheel wear per dress, and the wheel life on the De.op value were investigated. Moreover, the impacts of the cost components such as the machine tool hourly rate and the grinding wheel cost on the De.op value were given. Based on that, a mathematical model was proposed to determine the De.op value. The predicted De.op value was also verified by an experiment. The obtained result shows that the difference between the experimental De.op value and the predicted De.op value is within 1.7%, indicating that the mathematical model proposed in the study is reliable.

This paper shows an optimization study on calculating the optimum replaced wheel diameter in internal grinding of stainless steel. In this work, the effects of the input factors, including the initial diameter, the grinding wheel width, the ratio between the length and the diameter of the work-pieces, the dressing depth of cut, the wheel life and the radial grinding wheel wear per dress on the optimum replaced grinding wheel diameter were considered. Also, the effects of cost components, including the cost of the grinding machine and the wheel cost were examined. Moreover, to estimate the influences of these parameters on the optimum replaced diameter, a simulation experiment was given and conducted by programming. From the results of the study, a regression equation was proposed to calculate the optimum replaced diameter.

This paper examines potential cost savings attained through the flexible operation of water treatment plants and drinking water pumping and the benefits gained by investing in extended water storage capacities that enhance flexibility. An existing plant in South Germany serves as an example. Mixed-integer linear programming is used to model the plant and the electricity procurement on the German spot market while ensuring the security of supply. Cost optimization considering the different cost components reveals potential cost savings through flexible operation. The paper evaluates the benefits of building additional water reservoirs and rates the financial effects of the measures used to enhance the flexibility of water pumping and treatment plants.

This article presents a study of optimal control strategies for an energy storage system connected to a network of electrified Rubber Tyre Gantry (RTG) cranes. The study aims to design optimal control strategies for the power flows associated with the energy storage device, considering the highly volatile nature of RTG crane demand and difficulties in prediction. Deterministic optimal energy management controller and a Model Predictive Controller (MPC) are proposed as potentially suitable approaches to minimise the electric energy costs associated with the real-time electricity price and maximise the peak demand reduction, under given energy storage system parameters and network specifications. A specific case study is presented in to test the proposed optimal strategies and compares them to a set-point controller. The proposed models used in the study are validated using data collected from an instrumented RTG crane at the Port of Felixstowe, UK and are compared to a standard set-point controller. The results of the proposed control strategies show a significant reduction in the potential electricity costs and peak power demand from the RTG cranes.

Plate Fin Heat Sinks (PFHS) are among the simplest and most widespread devices for electronics cooling. Because of the many design parameters to be considered, developing both cost and thermal effective PFHS is a critical issue. Here, a novel thermal model of PFHS is presented. The model has a broad field of applicability, being comprehensive of the effects of flow bypass, developing boundary layers, fin efficiency and spreading resistance. Experiments are then carried out to validate the proposed thermal model, and its good accuracy is demonstrated. Finally, an optimization methodology based on genetic algorithms is proposed for a cost-effective selection of the design parameters of PFHS, which is particularly effective with semi-active configurations. Such an optimization methodology is then tested on a commercial heat sink, resulting in a possible 53% volume reduction at fixed thermal performances.

This study suggests an optimization method for the life cycle cost (LCC) in an economic feasibility analysis when applying energy saving techniques in the early design stage of a building. Literature and previous studies were reviewed to select appropriate optimization and LCC analysis techniques. The energy simulation (Energy Plus) and computational program (MATLAB) were linked to provide an automated optimization process. From the results, it is suggested that this process could outline the cost optimization model with which it is possible to minimize the LCC. To aid in understanding the model, a case study on an industrial building was performed to outline the operations of the cost optimization model including energy savings. An energy optimization model was also presented to illustrate the need for the cost optimization model.

: Deployment or distribution of valuable medical resources has emerged as an increasing challenge to hospital administrators and health policy makers. The hospital emergency department (HED) census and workload can be highly variable. Improvement of emergency services is an important stage in the development of the healthcare system and research on the optimal deployment of medical resources appears to be an important issue for HED long-term management. HED performance, in terms of patient flow and available resources, can be studied using the queue-based approach. The kernel point of this research is to approach the optimal cost on logistics using queuing theory. To model the proposed approach for a qualitative profile, a generic HED system is mapped into the M/M/R/N queue-based model, which assumes an R-server queuing system with Poisson arrivals, exponentially distributed service times and a system capacity of N. A comprehensive quantitative mathematical analysis on the cost pattern was done, while relevant simulations were also conducted to validate the proposed optimization model. The design illustration is presented in this paper to demonstrate the application scenario in a HED platform. Hence, the proposed approach provides a feasibly cost-oriented decision support framework to adapt a HED management requirement., Competing Interests: The authors declare no conflicts of interest.