Development Of Optimization Model Research Articles

Cost-aware renewable energy management: Centralized vs. distributed generation

We propose optimization strategies for cooperating households equipped with renewable energy assets and storage devices. We consider two system configurations: In the first configuration, households share access to an energy farm, where electricity is generated from renewable sources and stored in battery banks. In the second configuration, households are equipped with their own renewable energy sources and storage devices, and are allowed to share energy through the grid. The developed optimization model takes into account location and time-varying energy prices as well as energy transfer fees. To design our strategies, we first establish performance bounds, and compare the two configurations in terms of achievable savings and usability of renewable energy. Then, we devise real-time energy management algorithms by incorporating forecasting techniques in the proposed framework. Simulation results show that the proposed strategies outperform existing solutions by up to 10%. It is also shown that cooperative strategies outperform greedy approaches by up to 6.8%.

Dynamic control of urban sewer systems to reduce combined sewer overflows and their adverse impacts

Sewer network planners use control algorithms, based on optimization techniques, to control urban wastewater systems. These control algorithms have been used to ease the stress on the sewer networks and then, to reduce or to minimize the combined sewer overflows (CSOs). CSOs are not only risking human health but also adversely affecting the aquatic lives. Therefore, many cities try to avoid CSOs. However, this cannot be done to the perfect level due to the capacity limitations of the existing combined sewer networks. In addition, climate variabilities have caused unpredictable precipitation increments and therefore, the control is extremely difficult. Therefore, considering the spatial and temporal variations of runoffs and qualities of stormwater generated from the precipitation, an enhanced optimal control algorithm is illustrated in this paper to control the existing combined sewer networks. Minimizing the pollution load to the receiving water and minimizing the cost of wastewater treatment and pump operation are the two objective functions in the developed optimization algorithm. The algorithm was then successfully applied to a real-world combined sewer network in Liverpool, United Kingdom. Results reveal that the developed optimal control model is capable of handling the dynamic control settings of combined sewer system to minimize the two objective functions simultaneously. With a little computational appreciation, the developed optimal control model can be well-used in the real-time control of combined sewer networks.

A game theoretical low impact development optimization model for urban storm water management

Abstract This study presents a novel framework to optimize Low Impact Development (LID) practices for urban storm water management. First, the Storm Water Management Model (SWMM) model was executed for different possible scenarios of input parameters and various LIDs to simulate runoff volume, Biochemical Oxygen Demand (BOD) and Total Suspended Solids (TSS) loads. Next, a neural network (MLP-ANN) was trained and validated, as a surrogate model, against the set of inputs and output variables from the SWMM model simulations. The inherent uncertainties in the rainfall-runoff modeling were accounted for using a Nonlinear Interval Number Programming (NINP) model, and stakeholders' interactions are considered using a leader-follower game model. Velenjak urban watershed in Tehran, Iran, with four key stakeholders was considered as the study area. Tehran Municipality is the financial service provider that makes the first decision the leader-follower structure, and is considered as the leader with the priority of minimizing LIDs’ construction and maintenance costs. Tehran Department of Environmental Protection, Tehran Regional Water company, and Tehran Province Water and Wastewater company are the main followers in the decision making structure in the study area. This game theoretical framework yields several Pareto optimal solutions given the conflicting utilities of various players, and a Multi Criteria Decision Making procedure – i.e. PROMETHEE model – selects the most preferred compromised option. Fourteen weighting scenarios were considered in the PROMETHEE model to determine the compromise solution among the 52 solutions on the trade-off curve. The novelty of this study lies in using a nonlinear interval conflict resolution multi-objective optimization model for urban storm water management based on a leader-follower game. The proposed methodology warrants BOD and TSS loads, as well as storm water volume, are all reduced, with the highest reductions of 93, 86 and 90 percent, respectively. Results testify to the efficacy of the proposed model for urban storm water management.

Multiportfolio optimization with CVaR risk measure

In the financial industry, the trading of multiple portfolios is usually aggregated and optimized simultaneously. When multiple portfolios are managed together, unique issues such as market impact costs must be dealt with properly. Conditional Value-at-Risk (CVaR) is a coherent risk measure with the computationally friendly feature of convexity. In this study, we propose the new combination of CVaR with the multiportfolio optimization (MPO) problem and develop optimization models with using CVaR to measure risks in MPO problems. A five-step scheme is presented for practical operations with considering the impact costs caused by aggregating the trading of multiple portfolios. The impact of CVaR on returns and utility in MPO environment is studied, and the comparisons with existing methods and sensitivity analysis are reported.

Considering deterioration propagation in transportation infrastructure maintenance planning

Civil infrastructure system maintenance planning is to determine which facility should be repaired, when and how maintenance should be carried out, and what treatment should be used under budget and other resource constraints. In the existing literature, various simulation and optimization models have been developed to help select the optimal maintenance plan. However, the developed models overlooked the deterioration propagation between adjacent connected facilities of the network infrastructure system. For instance, a facility receiving zero maintenance or having a failure of maintenance treatment affects not only the condition of itself, but also the deterioration rate of its neighboring facilities. This raises the call for taking the deterioration propagation into consideration when developing optimization models and capture to which extent it can affect the optimal maintenance plan. Therefore, in this paper, an infrastructure maintenance planning model considering the deterioration propagation between facilities is formulated as a mixed integer linear programming problem. A heuristic algorithm was proposed to solve the problem efficiently. Example networks were tested for the performance comparison between CPLEX and the heuristic algorithm. The proposed model performs better than models without the deterioration propagation. • Deterioration propagation should be considered in asset management. • Formulated an integer linear optimization model to find optimized maintenance scheduling. • Models considering deterioration propagation performed better than those that don't.

A new strategy based on approximate dynamic programming to maximize the net revenue of IaaS cloud providers with limited resources

Abstract With rapid development of cloud computing, more conventional Internet Data Centers get involved into this business model. Providers of small and medium sized data centers have relatively limited computing resources compared to global IaaS providers such as Amazon. The demand for computing at these service providers may be above the available capacity especially during peak shopping periods (e.g. mother’s day or singles’ day). However, it is undesirable to blindly expand capacity, which will not only incur the purchase cost of new machines but also the maintenance costs associated with resource over-provisioning. For these service providers, the most important thing is how to effectively manage resources during peak demand periods to maximize their net revenue. In this context, we focus on the joint admission control and virtual machine placement problem, and propose a dynamic optimization model considering that customers dynamically create and terminate virtual machines in cloud computing environment. In terms of problem complexity, the developed dynamic optimization model typically suffers from “curse of dimensionality” and is computationally intractable. So we resort to approximate dynamic programming framework and propose a new strategy to yield a tractable model for real-time decision-making. Extensive simulations are conducted to compare our proposed strategy with other strategies including existing methods of virtual machine placement. The simulation results demonstrate that our customized strategy achieves substantial revenue improvements and the improvement is more significant as resource provisioning is tighter. In addition, it can be scalable to solve large cases up to 1000 physical machines and yield a real-time decision in a reasonable time.

Optimizing the reuse of reclaimed water in arid urban regions: A case study in Urumqi, Northwest China

Water resources plays an essential role in the socio-economic development in the city of Urumqi, which is the largest city located in the arid region in Northwest China. Due to the declining availability of freshwater resources, reclaimed water has become increasingly prominent as an alternative water source. In this study, the status of wastewater treatment and reclaimed water utilization in Urumqi was investigated and a set of measures for maintaining the quality and quantity of reclaimed water were proposed. After analyzing the reclaimed water supply and urban landscape water demand in Urumqi, we developed optimization models to examine three water distribution options to supply reclaimed water for the urban landscape. Based on the study results, the least costly transportation plan that was capable of meeting the amount of water required for the landscape in the Urumqi was recommended. This case study provides a scientific basis for the implementation of measures to efficiently use reclaimed water and maintain ecological security in arid Northwest China.

The multi-objective optimization of combustion system operations based on deep data-driven models

Advancing methods for modeling combustion systems and optimizing their operations is beneficial to improve the combustion performance. This paper develops a deep data-driven framework for the optimization of combustion system operations. First, a deep belief network based method is developed to model both of the combustion efficiency and the NOx emission. Next, a multi-objective optimization model is developed by integrating the deep belief network based models, the considered operational constraints, and the control variable constraints. Two objectives, maximizing the combustion efficiency and minimizing the NOx emission, are considered in the optimization. Due to the nonlinearity and complexity of the optimization model, traditional exact solution methods are not applicable to solve it. A recently presented swarm intelligence method, the JAYA algorithm, is applied to obtain the optimal solutions of the developed optimization model. Advantages of using JAYA are proved by benchmarking against well-known computational intelligence methods. The feasibility and effectiveness of the developed framework for optimizing the combustion process using industrial data is validated by computational experiments. Results demonstrate the potential of further improving both of the combustion efficiency and NOx emission by optimizing control settings of the combustion system.

A Grading System-based Model for Detecting Unbalanced Bids during the Tendering Process

Unbalanced bidding is a common practice used in both unit price and lump sum contracts. Contractors may unbalance their bids in different forms for various reasons. The studies in the literature either focus on developing optimization models that assist contractors in winning contracts and maximizing profits of their bids through unbalancing or developing models that assist owners in detecting and/or preventing unbalanced bids during the bid evaluation stage. Unbalanced bidding is one of the most controversial subjects in the construction management literature and practice. Although there is no consensus on whether it is unethical or not, this practice is not usually for the benefit of owners. Therefore, owners have the right to reject the unbalanced bids and create a fair competition environment if they have a mechanism to detect it during the bid evaluation process. The main objective of this study is to propose a model, which consists of five different grading systems and helps owners in detecting unbalanced bids during the tendering process. In the proposed model, owners may either calculate the individual grades of each bidder or calculate the final score of each bidder by assigning different weights to these grading systems according to the project characteristics or their own needs. The final scores and bid prices of the contractors can be simultaneously evaluated. In order to demonstrate the applicability of the proposed model, an illustrative example is presented. It can be concluded that the proposed model can be effectively and easily used by owners for detecting unbalanced bids. This paper is the revised version of the paper that has been published in the proceedings of the Creative Construction Conference 2018 (Polat et al., 2018).

Toward Detection of Distribution System Faulted Line Sections in Real Time: A Mixed Integer Linear Programming Approach

With smart fault indicators (SFIs) being widely deployed in distribution networks, availability of SFIs statuses and their current measurement provides an opportunity to locate distribution system faulted line sections in real time. However, the integrity of SFIs data together with complex fault scenarios poses a challenge for fault diagnosis, especially with the presence of distributed generators (DGs). To solve this issue, this paper proposes a new method based on mixed integer linear programming (MILP) for identification of the faulted line sections using incomplete and incorrect SFIs statuses. By minimizing the discrepancy between the expected SFI statuses and the available evidence at the distribution operating center, the proposed approach is able to handle complex fault scenarios with multiple faults and failures or malfunctions of SFIs. When DGs are present, the current measurement of SFIs and their statuses are jointly used. The result is a robust algorithm for detection of faulted line sections of distribution systems with DGs in real time. In addition, a new technique is proposed to convert nonlinear logical constraints into a linear combination of decision variables. The resulting linearity of the developed optimization model ensures its optimality. Simulation results based on a utility feeder demonstrate the effectiveness of the proposed approach.

Integrated optimization with building information modeling and life cycle assessment for generating energy efficient buildings

Energy consumption in buildings is a very important issue, where the operational demand is considered to be one of the highest amongst all other sectors of an economy. Moving towards energy efficient buildings is a key factor to achieve sustainability. A novel framework for integrating mathematical optimization, Building Information Modeling, and Life Cycle Assessment to enhance the operating energy efficiency of the resulting building designs adopted, along with reducing the difficulties associated with the construction of the building, in terms of cost of construction, is developed. The framework accommodates various parameters, via integrating mathematical optimization programming, Building Information Modeling, and Life Cycle Assessment to improve the building performance, identify alternative sustainable designs, and empower the decision-making process and sustainability in the construction sector. Through the developed optimization model, the examination of various alternatives for building components that make up the envelope of a residential building is undertaken. Insights gained from the results show that all components of building envelopes influence the energy consumption in buildings, particularly, exterior walls and windows. Impacts in terms of annual energy use intensity can be reduced by about 45%, life cycle energy use and cost can be enhanced by more than 50%, and environmental impacts such as acidification and global warming potential can be reduced by more than 30%, due to use of the proposed framework. This work indicates that sustainable building decisions can be achieved by optimizing the material selection and assessment of environmental impact via Building Information Modeling and life cycle assessment.

Service load balancing, scheduling, and logistics optimization in cloud manufacturing by using genetic algorithm

SummaryRecent years have seen a great deal of attention in the aspects of cloud manufacturing. Generally, in cloud manufacturing, the capabilities and manufacturing resources that distributed in different geographical places are virtualized and encapsulated into manufacturing cloud services. The literature confirms that applying queuing theory to optimize service selection and scheduling load balancing (SOSL) while taking into account logistics is still scarce and an open issue for practical implementation of cloud manufacturing. This reason motivates our attempts to present a cloud manufacturing queuing system (CMfgQS) as well as a load balancing heuristic algorithm based on task process times (LBPT), simultaneously among the first studies in this research area. Hence, a novel optimization model as mixed‐integer linear programming is developed by implementing both CMfgQs and LBPT. Due to the natural complexity of the problem proposed, this study applies a genetic algorithm to solve the developed optimization model in large instances. Finally, the computational results ensure the effectiveness of the proposed model as well as the performance of the employed heuristic algorithm.

Optimization of a regional energy system including CHP plants and local PV system and hydropower: Scenarios for the County of Västmanland in Sweden

Providing the energy needs of the cumulatively increasing population has become a challenge for the regional energy systems in the world. The most critical challenge is to supply enough energy in the forms of heat and power during the cold and warm periods of the year with the lowest production cost and minimum environmental impacts. A solution is to increase the green energy supply from renewable energy resources such as solar, wind power, and hydropower. In order to apply this solution in the real energy system, potentials for clean energy supply in an optimized manner should be evaluated. In this study, an optimization model is developed for a regional energy system in the central part of Sweden. The studied system consists of Combined Heat and Power (CHP) plants and heat water boilers together with renewable energy supply from rooftop Photo Voltaic (PV) - solar collectors and regional hydropower plants. The General Algebraic Modeling System (GAMS) is used to create the model based on the Mixed Integer Linear Programming (MILP) method. The goal is to evaluate the influence of local renewable energy systems on the production planning of CHP plants in a region. Two different scenarios are investigated based on the extremes in energy supply and demand concerning the increased use of Electrical Vehicles (EVs) and more application of Heat Pumps (HPs) in the system. The results show that installation of rooftop PV systems has the potential to reduce the electricity import to the region; however, it will at the same time reduce the operation time of the CHP plans during the summer period. With increased use of HPs for heating, the shut off time for CHP plants is further increased. Increase in electric passenger cars penetration in the system has no impacts on the production profiles of the plants. The regional electricity demand grows significantly by more utilization of EVs and increased application of heat pumps in the studied system. The high electricity demand will mainly be satisfied by importing electricity from outside the region together with low production from CHP plants and the power generated by the rooftop PV systems and regional hydropower. The developed optimization model with studied scenarios can be applied to other energy systems to increase the knowledge of production planning and feasibility of a fossil fuel free energy system.

Optimal Re-Dispatching of Cascaded Hydropower Plants Using Quadratic Programming and Chance-Constrained Programming

Stochastic production from wind power plants imposes additional uncertainty in power system operation. It can cause problems in load and generation balancing in the power system and can also cause congestion in the transmission network. This paper deals with the problems of congestion in the transmission network, which are caused by the production of wind power plants. An optimization model for corrective congestion management is developed. Congestions are relieved by re-dispatching several cascaded hydropower plants. Optimization methodology covers the optimization period of one day divided into the 24 segments for each hour. The developed optimization methodology consists of two optimization stages. The objective of the first optimization stage is to obtain an optimal day-ahead dispatch plan of the hydropower plants that maximizes profit from selling energy to the day-ahead electricity market. If such a dispatch plan, together with the wind power plant production, causes congestion in the transmission network, the second optimization stage is started. The objective of the second optimization stage is the minimization of the re-dispatching of cascaded hydropower plants in order to avoid possible congestion. The concept of chance-constrained programming is used in order to consider uncertain wind power production. The first optimization stage is defined as a mixed-integer linear programming problem and the second optimization stage is defined as a quadratic programming (QP) problem, in combination with chance-constrained programming. The developed optimization model is tested and verified using the model of a real-life power system.

Maximizing profit and responsiveness of a closed-loop supply chain in presence of product exchange offer

Designing an effective collection process is strategically important due to its impact on product-recovery, profit and responsiveness of overall closed-loop supply chain (CLSC). In this paper, we propose a product-recovery framework which models consumers’ willingness to accept a product exchange offer (PEO) as non-linear function of discount amount offered to them on returning used product and utility they obtain on receiving the new product. The proposed recovery framework is then integrated with a multi-objective optimization model, which takes into account the responsiveness of both forward and reverse supply chain, in order to maximize profit and responsiveness of the overall CLSC. The optimization model is solved using a modified interactive fuzzy goal programming technique, which allows the decision makers to adjust lower and/or upper bound of the objective functions during the interactive decision making process. The developed optimization model determines optimal value of discount, optimal return and remanufacturing quantity of used products, and optimal manufacturing quantity of fresh-new products. The analysis suggests a trade-off between amount of discount offered to consumers on returning used products and perceived quality of the new product while accepting the PEO.

Optimization models of a two-component nuclear energy system with thermal and fast reactors in a closed nuclear fuel cycle

The article presents a description and some illustrative results of the application of two optimization models for a two-component nuclear energy system consisting of thermal and fast reactors in a closed nuclear fuel cycle. These models correspond to two possible options of developing Russian nuclear energy system, which are discussed in the expert community: (1) thermal and fast reactors utilizing uranium and mixed oxide fuel, (2) thermal reactors utilizing uranium oxide fuel and fast reactors utilizing mixed nitride uranium-plutonium fuel. The optimization models elaborated using the IAEA MESSAGE energy planning tool make it possible not only to optimize the nuclear energy system structure according to the economic criterion, taking into account resource and infrastructural constraints, but also to be used as a basis for developing multi-objective, stochastic and robust optimization models of a two-component nuclear energy system. These models were elaborated in full compliance with the recommendations of the IAEA’s PESS and INPRO sections, regarding the specification of nuclear energy systems in MESSAGE. The study is based on publications of experts from NRC “Kurchatov Institute”, JSC “SSC RF-IPPE”, ITCP “Proryv”, JSC “NIKIET”. The presented results demonstrate the characteristic structural features of a two-component nuclear energy system for conservative assumptions in order to illustrate the capabilities of the developed optimization models. Consideration is also given to the economic feasibility of a technologically diversified nuclear energy structure providing the possibility of forming on its base a robust system in the future. It has been demonstrated that given the current uncertainties in the costs of nuclear fuel cycle services and reactor technologies, it is impossible at the moment to make a reasonable conclusion regarding the greatest attractiveness of a particular option in terms of the economic performance.

Optimization-based assessment framework for carbon utilization strategies: Energy production from coke oven gas

This study aims to develop an optimization-based framework to systematically analyze and assess carbon utilization strategies. To achieve this goal, we generated a superstructure consisting of a number of carbon utilization strategies, which included a series of technologies (separation and reaction) to convert carbon sources into final products. We then estimated the technical and economic parameters of all the included technologies using interactive process models. The flow and relationship information in the superstructure and the technical and economic parameters from the process models are used to develop optimization models capable of assessing the carbon utilization strategies with four different criteria: product quantity, energy efficiency, production cost, and revenue. With the proposed framework, we can answer a range of interesting questions about carbon utilization – what is the best strategy for the production of a certain product or for the utilization of a given carbon source? The capability of the proposed framework is illustrated with a coke oven gas case study. As a result, the optimal production strategies of hydrogen, methane, methanol and liquid fuels are identified using four different criteria.

Developing Statistical Optimization Models for Urban Competitiveness Index: Under the Boundaries of Econophysics Approach

The purpose of this research was to establish the urban competitiveness index (UCI) by using the statistical optimization method for the econophysics approach. With this technique, economic data regarding urban areas and the factors affecting UCI have been determined. The research covers 30 urban centres located in 15 countries worldwide. Urban centres with the gross domestic product per capita of $10,000 or more were taken into consideration. The significant levels of the factors were determined with the statistical optimization method, and optimum values were calculated with the developed optimization models. Re‐index values were calculated and compared with the results of PricewaterhouseCoopers and World Economic Forum. According to the results, the high UCI value of these locations depends not only on economic data but also on high values of social factors. Thus, those locations are becoming the centre of attraction for investments and capital with increasing competitiveness.

The model of vehicle optimum quantity for grain crop harvesting under the conditions of farming in Poltava region

The previously developed optimization model for determining the required number of vehicles as part of the harvest transportation complex during grain crop harvesting for the conditions of grain farming in Poltava region has been applied. A computational methodology was developed to determine the design parameters depending on the farm conditions included in the expression for the target function of the optimization model constructed, namely, average number of arrival rate per time and waiting time in queue (excludes service time) for each individual customer. Using the queueing theory methods, a ratio of the number of harvesting and transport equipment in the harvest transportation complex composition was found, which provides the minimum total costs (corresponding to the target function of the previously proposed model), which are due to the downtime of combine harvesters and the cost of vehicle maintaining. It has been established that with the increase in the vehicle number, the total costs of downtime of combine harvesters and vehicles decrease at first and then increase.

ECONOMIC OPTIMIZATION OF SOCIO-EDUCATIONAL SERVICES PROVISION IN NEW RURAL SETTLEMENTS: CASE OF PERM SUBURB IN RUSSIA

Urban metropolitan areas continue to grow not only due to population growth in rural areas, but also by creating new settlements. The establishment of new settlements in the rural suburbs of major cities became widespread in Russia 15 years ago. For instance, 14 settlements formed around Perm in the past six years. Such settlements do not have any production (economic) importance by themselves. At the same time, the establishment of social infrastructure is not planned initially. At the first stage municipalities, in which new settlements form, receive significant economic benefit from the development of the rural suburban resettlement – village administrations budget is replenished by property and land taxes. For example, in the suburban district of Permskii Krai, through the establishment of new settlements, budget increased by 37-43% annually. The need of providing several new settlements with social facilities (e.g. schools, kindergartens, etc.) comes gradually. The establishment of such facilities within the boundaries of the rural municipalities is estimated at hundreds of millions Russian rubles. Thus, all the initial economic benefits from the development of new settlements are lost. Therefore, it appears that there is a need to develop optimization models of educational facilities within the boundaries of municipalities, in order to optimize their number, size and location. The aim of the developed model is minimizing both distance to educational facilities and cost of these facilities. Calculations show that the development of a regional optimization scheme of socio-educational environment of a rural settlement within the boundaries of the suburban zone will reduce expenditure on establishment and operation of these facilities on average by 20-25%.