Optimization modeling to support renewables integration in power systems

The growth of population and increasing consumption of electricity exposes countries to build additional power units. One of the most important issues in the field of power system is the generation expansion planning (GEP) problem. The objective of the GEP problem is to evaluate the least cost investment plan for addition of power generating units over a planning period subject to power demand and energy demand constraints. In this paper an optimization model with fuzzy parameters is used to express GEP problem. In proposed model both of power demand and energy demand are taken as fuzzy parameters. A Qualitative analysis of basic stability notions for the problem of concern is defined. These notations are the set of feasible parameters, the solvability set and the stability set of the first kind. A case study is provided to demonstrate the efficiency of proposed model. A comparison study between our approach and the deterministic one is made.

Computation of Loss of Load Probability (LOLP) is a challenge in Generation Expansion Planning (GEP) problem. This paper presents a dynamic GEP model considering LOLP as a reliability criterion. The objective function of the proposed dynamic GEP model includes the discounted total costs of investment cost, operation cost, and maintenance cost. The proposed LOLP constrained GEP problem is formulated as a mixed integer nonlinear programming (MINLP) problem. The MINLP formulation of GEP problem is then converted to a mixed integer linear programming (MIP) problem by applying several approximations to LOLP constraint. The utilized approximations are divided into two clusters for low and high order outages. A test case containing 12 types of installed and 5 types of candidate plants with a 14-year planning horizon is used to validate the efficiency of the proposed dynamic GEP problem. The developed MIP-based GEP model is solved using CPLEX solver and the obtained results are compared with the conventional LOLP-constrained GEP model.

This paper presents the generation expansion planning (GEP) problem in a competitive environment. In the competitive market, each power producing companies are called as generating companies (GENCOs) and they compete with each other to maximize their own profits. But the objective of the regulatory body is to stabilize the market through coordination between GENCOs by providing long term market information to GENCOs, which prevents extreme over/under investments in the electricity market. The regulatory body is also responsible for optimal generation capacity, while considering system security, national security (fuel mix ratio), social welfare and reserve margin. In this paper, the GEP problem is solved with a realistic test system comprising of 5-GENCOs. Each player performs self-optimization using particle swarm optimization (PSO) technique, assuming the strategies of other players are fixed. The competitive GEP problem is solved iteratively by self-optimizing (maximizing profit) each GENCO in turn, while satisfying the constraints.

This paper describes use of a multiobjective optimization method, elitist nondominated sorting genetic algorithm version II (NSGA-II), to the generation expansion planning (GEP) problem. The proposed model provides for decision maker choice from among the different trade-off solutions. Two different problem formulations are considered. In one formulation, the first objective is to minimize cost; the second objective is to minimize sum of normalized constraint violations. In the other formulation, the first objective is to minimize investment cost; the second objective is to minimize outage cost (or maximize reliability). Virtual mapping procedure is introduced to improve the performance of NSGA-II. The GEP problem considered is a test system for a six-year planning horizon having five types of candidate units. The results are compared and validated.

Generation expansion planning with wind power plant using DE algorithm

We present a framework for capacity allocation decisions for assembly-test (A-T) facilities that is comprised of an optimization model and a simulation model. The optimization and simulation models are used iteratively until a feasible and profitable capacity plan is generated. The models communicate using an automated feedback loop and at each iteration the model parameters are adjusted. We describe the role of the optimization model, the simulation model and the feedback loop. Once the capacity plan is generated, it is passed down to the shop-floor for implementation. Hence, decision makers can develop accurate and more profitable execution level capacity plans using the integrated model which utilizes both optimization and simulation models. In this paper, we focus on the optimization model for capacity planning for the entire A-T facility at the individual equipment (resource) level for a two-week planning period and briefly discuss the simulation and the adjustment model.

The objective of this paper is the development and solution of nonlinear and mixed-integer (MIP) optimization models for real-world planning and scheduling problems in petroleum refineries. Firstly, we present a nonlinear planning model that represents a general refinery topology and allows implementation of nonlinear process models as well as blending relations. The optimization model is able to define new operating points, thus increasing the production of the more valuable products and simultaneously satisfying all specification constraints. The second part addresses scheduling problems in oil refineries, which are formulated as MIP optimization models and rely on both continuous and discrete time representations. Three practical applications closely related to the current refinery scenario are presented. The first one addresses the problem of crude oil inventory management of a refinery that receives several types of crude oil delivered exclusively by a single oil pipeline. Subsequently, two optimization models intended to define the optimal production policy, inventory control and distribution are proposed and solved for the fuel oil and asphalt plant. Finally, the planning model of Moro et al. (1998) is extended in order to sequence decisions at the scheduling level in the liquefied petroleum gas (LPG) area for maximization of the production of petrochemical-grade propane and product delivery.

Purpose: Rotational IMRT delivery techniques, such as volumetric modulated arc therapy (VMAT), are alternatives to standard IMRT delivery, claiming faster delivery and equally good dose distributions. In reality there is a cost to faster delivery times. The purpose of this investigation is to understand the tradeoff between plan quality and treatment time in rotational delivery by use of optimization models. Method and Materials: We study a simplified VMAT model for a 2D phantom and construct an exact mixed integer optimization model for it. The model includes treatment time as a constraint, and otherwise does not specify how the delivery is to be done, allowing for either VMAT or IMRT solutions. The model is too large to solve, but we trap the optimal solution between tight upper and lower bounds with additional models. The lower bound solutions are used to warm start the upper bound solutions. We construct Pareto surfaces for the joint tradeoffs of treatment time, tumor maximum dose, and organ at risk mean dose. Results: Upper bounds are within 10% of lower bound solutions. This allows us to conclusively demonstrate that 1) the treatment time constraint becomes more important when high intensity modulation is required to achieve optimal dose distributions, and 2) finding a good feasible VMAT plan is benefitted (by ∼5% in objective value) by warmstarting using the fluence modulation required at each angular location. Conclusion: For problems where the optimal dose distribution has non‐convex iso‐dose lines (to avoid nearby critical structures), the high modulation necessary to achieve such plans translates into longer treatment times. In these cases, when allowed treatment time is small, the optimal solutions show fluence being delivered at every angle — VMAT. If treatment time is allowed to be larger, IMRT plans are found to be optimal.

Typical applications of LCA assume that the magnitude of life-cycle impact grows proportionally to the volume of demand, while in reality the additional impact due to marginal increase in demand may differ from the average impact. In the literature, the calculation of marginal life-cycle impacts often involves the use of optimization models, where typically the total economic costs are minimized. However, modeling spatially explicit marginal responses of a system involving multiple producers and consumers has not been discussed in LCA literature. In this paper, we demonstrate a spatial optimization technique for modeling marginal responses of a multi-producer, multi-consumer system. Our model determines the optimal production-by-location mix and associated environmental stressor at minimum systems cost. We demonstrate the model using a preliminary case study on blue water consumption by potato. We collected state-by-state data on potato yield, cost of potato production, and water use for irrigation, as well as interstate transportation fuel costs. We also estimated the marginal increase in demand for potato following USDA's recommended diet. The results show that the cradle-to-gate blue water consumption of potatoes based on 2016 demand was 96 m3/ton potato, which changes non-linearly along with the growth of potato demands. In order to meet the USDA's recommended diet, the additional demand on potato (530,000 ton per year) would result in a 29% lower blue water consumption per ton of potato (68 m3/ton) as compared to the average result of the current production system. In addition, we tested the model to analyze the marginal impacts under two scenarios: (1) high fuel tax and (2) high water price. The preliminary results indicate that water pricing is more effective than a fuel tax increase in reducing the marginal blue water consumption of potato based on our scenarios of the recommended diet demand. The results demonstrate that our model can be used to understand the non-linear behavior of marginal effect over demand crease, and for testing alternative policy scenarios involving a system with multiple producers and consumers across regions.

The considerable increase in the complexity associated with the formulation of maintenance plans has enabled the development of new techniques to bring maintenance scheduling optimization models to more realistic environments. In this sense, a previous optimization model was proposed considering the use of time windows for the formation of grouping schemes under an opportunistic strategy for maintenance activities considering non-negligible execution times, thus offering the possibility of analysing scenarios with limited resources. This article proposes a risk analysis based on the failure probability of each component involved in the maintenance scheduling optimization model, which has the particularity of enabling a greater number of combinations of grouped PM activities. Moreover, it seeks to identify the general behaviour of the optimization model against different scenarios of periodicities and execution times of each maintenance activity. The proposed optimization model is formulated under a mixed integer linear programming (MILP) paradigm and its objective function seeks to minimize the unavailability of the system associated with the execution times of the activities developed, generating different experimental cases, and varying the start time scheduling under a tolerance factor from 0% up to a maximum of 25% for advance or delay. Results show in contrast with the base optimization model, an 8% less unavailability when the tolerance factor is 10%. Finally, it was possible to quantify the risk present in each maintenance schedule, at the same time a behaviour towards advancing PM activities is evidenced by the optimization model proposed over the delay.

Software-defined networking provides a promising solution for traffic engineering (TE) by utilizing a centralized controller to remotely configure programmable network switches. In order to achieve optimal traffic distribution and meet the Quality of Service (QoS) requirements, the SDN controller must frequently solve complex optimization models. Applying an optimization model in ISP (Internet Service Provider) networks implies abundant routing reconfigurations, which adversely impact the network stability and QoS parameters. Namely, since routing updates usually cannot be applied on all switches at the same time, transient congestion and loops often happen as a result. Here, in this paper, we propose a new control framework that strives to maximize the network throughput and provide QoS with minimal reconfiguration costs. In contrast to the conventional TE approaches, which perform the network optimizations periodically and control the side effects of reconfigurations by carefully choosing the period length between the optimization cycles, we propose a multi-objective optimization model which jointly minimizes the routing cost function and the reconfiguration overhead. The Pareto frontier of the optimization model is generated by the augmented ∈-constrained method, whereas a Lyapunov drift-plus-penalty function is used to select the best compromise solution from the Pareto set. Since the reconfiguration overhead is reduced, the network controller could be allowed to optimize resource allocation more frequently, in order to respond quickly and efficiently to the network changes. Considering that the proposed optimization model is computationally intractable in large-scale networks, we also propose a heuristic algorithm to efficiently solve large instances of the problem. In simulations and Mininet experiments, we show that our solution brings performance improvement over the conventional periodic TE techniques. Moreover, the proposed heuristic achieves better QoS request acceptance ratio than the state-of-the-art multi-objective optimization solution, despite the significantly reduced computational complexity.

<p>In practice, manufacturing systems are highly dynamic and continuously facing unexpected events and interruptions. This puts production managers in charge of making frequent updates to the ongoing plans and schedules to cope with these changes. This is done by adopting different scheduling strategies and optimization models. Despite several attempts in the literature, the need for models that can minimize the effect of changes (i.e., stochastic and robust models) or react to them using real-time information (i.e., real- time optimization models) is still felt. </p> <p>This dissertation includes three main contributions with different optimization models and heuristic algorithms that can help managers deal with different unexpected events and interruptions on the shop floor. In the first contribution (Chapter 2), the case of stochastic deterioration-based failures in a single machine production system is considered. The machine’s degradation is modeled as a multi-state system. The obtained formulations are then integrated into an optimization model that jointly optimizes the production sequences, machine inspections, and condition-based maintenance actions. The results showed an average improvement of about 35% in total expected costs when information about the machine’s degradation level was used. </p> <p>In the second contribution (Chapter 3), the case of unexpected new job arrivals and random machine breakdowns in a flexible job shop production environment is considered. The effect of random machine breakdowns on the processing durations is formulated and then integrated into a dynamic optimization model. The proposed model investigates how real-time updates can be utilized to improve scheduling decisions based on unexpected arrivals, the availability of machines (downtimes and recovery times), and the completion times of operations. </p> <p>Finally, in Chapter 4, the case of integrating production scheduling and condition-based preventive maintenance (PM) planning in a flexible job shop production system is addressed. The study considers the case of stochastic machine degradation, random machine breakdowns, minimal repairs, condition-based PM, due date changes, and new job arrivals. The reliability of machines is modeled as a multi-state system, in which the obtained formulations are incorporated into an integrated dynamic optimization model. The developed model aims to study the effects of different RTS policies on each of the considered cases and empirically quantify the potential benefits of using real-time information to enhance scheduling decisions. </p>

During the last two decades, there has been a growing interest in component-based software engineering CBSE both in academia and industry. In component-based system development, it is common to identify software modules first. Once they are identified, we need to select appropriate software components for each module. These components can either be bought as commercial off-the-shelf COTS components and probably adapted to work in the software system or can be developed in-house. This is a 'build-or-buy' decision. This paper discusses a framework that helps a developer to decide whether to buy or to build software components while designing a fault-tolerant modular software system. This paper proposes optimisation models for optimal component selection for a fault-tolerant modular software system under the consensus recovery block scheme. It is necessary to identify critical modules in the design of a fault-tolerant modular software system and also to develop a system with a built in redundancy for critical modules. Therefore, the first optimisation model is developed for optimal component selection with the dual objective of reliability maximisation and cost minimisation of the overall system under the constraints on the delivery time and criticality of modules. The second optimisation model is an extension of the first optimisation model and discusses the issue of compatibility of components of modules. In practice, it is not possible for management to obtain precise value of reliability, cost, delivery time, etc., therefore both the models are formulated as fuzzy multi-objective optimisation models. A case study of developing a manufacturing system for medium-size enterprise is used to illustrate the proposed methodology.

The aim of the generation expansion planning(GEP) problem is to determine the type, size and time of construction of generating units to achieve minimum total costs of investment, generation, and maintenance. According to the adequacy criteria, to have a reliable power system the yearly peak load must be supplied with a predefined reserve margin. The forecasting of yearly peak load for a future time horizon has some degrees of error. To this end the generation expansion planning must be carried out to tolerate the uncertainty of yearly peak load. In this paper the GEP problem is formulated as a mixed integer non-linear programming(MINLP) model. The uncertainties of yearly peak loads is handled via the point estimate method. The proposed model is simulated over a test system is with a 14-year planning. The developed MINLP-based GEP model is solved using SBB solver and the obtained results are compared with the conventional GEP model.

Bolted connections are important components of steel structures and they must be based on good design, with analytical descriptions of failure mechanisms that are thoroughly researched. A robust and reliable analytical description of a failure mode requires a large base of results, collected experimentally or numerically, usually via FEA. The modelling procedure in FEM simulations can significantly influence result accuracy, and one of the most important parameters is spatial discretization. Mesh size presents a delicate balance between accuracy, efficiency and convergence issues, especially in bolted connection analysis with contact, material and geometric nonlinearity. Due to increased complexity, sensitivity studies are difficult to interpret and time expensive, which is why in this study a mesh size optimization procedure is presented. This optimization is based on a previously verified and validated numerical model parametric study of a single-bolt lap connection. The first step included forming of an optimization model based on an artificial neural network, through which results were extrapolated and compared to experimental. However, estimated solving wall time was significantly longer. In the second step, a nonlinear optimization model was structured to determine a mesh size for which the difference between experimental and optimization results would be equal, whilst having a minimal solving wall time. The optimization model provided insights into mesh size quality to efficiency ratios and rendered that the minimal difference between experimental and numerical results is 1.57%, while having a solving wall time of 1.3 h, instead of the step one optimization modelling estimated 36 h.