Showing papers on "Stochastic programming published in 2019"
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TL;DR: This study presents an integration perspective for developing a green and sustainable closed-loop supply chain (CLSC) network under uncertain demand with a bi-objective optimization model with two objectives for CO2 emissions and total operating cost.
138 citations
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TL;DR: Numerical results demonstrate the advantages of implementing stochastic programming on the UC problem by taking into account the intermittent behavior of wind energy and load inconstancy.
Abstract: This essay performs a reliability constraint stochastic model for unit commitment problem by considering generation and transmission constraints with high wind penetration and volatility of load demands. This query is expressed as a MILP that is based on the linear direct current model. The proposed approach models uncertainty of wind generators output power, load demand fluctuations and stochastic elements outage of the system like generators and transmission lines. In this paper, stochastic interdependence between random variables like wind speed and load demand is recognized. To establish the probability distribution of these correlated random variables, Copula theory is applied. Correlation structure between wind speed of different locations and a group of loads existing in the same area is investigated and studied based on historical data. For representing these uncertainties in the stochastic unit commitment problem, possible scenarios are generated with Monte Carlo simulation method. The reliability constraints are utilized in each scenario to evaluate the feasibility of solutions from a reliability point. The introduced stochastic UC is executed on the RTS 96-bus test system. Numerical results demonstrate the advantages of implementing stochastic programming on the UC problem by taking into account the intermittent behavior of wind energy and load inconstancy.
133 citations
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TL;DR: The downside risk constraints (DRC) are proposed to minimize the risk associated with uncertainties in order to obtain the risk-based scheduling of energy hub via scenario-based stochastic programming and the effect of the DRP on the problem is investigated and the results show that the expected cost and RIC are decreased.
109 citations
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TL;DR: In this article, an integrated operational model for electricity and natural gas systems under uncertain power supply by applying two-stage stochastic programming is proposed to optimize day-ahead and real-time dispatch of both energy systems and aims at minimizing the total expected cost.
102 citations
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TL;DR: This work proposes a new type of decomposition algorithm, based on the recently proposed framework of stochastic dual dynamic integer programming (SDDiP), to solve the multistage stochastics unit commitment (MSUC) problem and proposes a variety of computational enhancements to SDDiP.
Abstract: Unit commitment (UC) is a key operational problem in power systems for the optimal schedule of daily generation commitment. Incorporating uncertainty in this already difficult mixed-integer optimization problem introduces significant computational challenges. Most existing stochastic UC models consider either a two-stage decision structure, where the commitment schedule for the entire planning horizon is decided before the uncertainty is realized, or a multistage stochastic programming model with relatively small scenario trees to ensure tractability. We propose a new type of decomposition algorithm, based on the recently proposed framework of stochastic dual dynamic integer programming (SDDiP), to solve the multistage stochastic unit commitment (MSUC) problem. We propose a variety of computational enhancements to SDDiP, and conduct systematic and extensive computational experiments to demonstrate that the proposed method is able to handle elaborate stochastic processes and can solve MSUCs with a huge number of scenarios that are impossible to handle by existing methods.
99 citations
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TL;DR: A novel stochastic game-based shared control framework to model the steering torque interaction between the driver and the intelligent electric power steering (IEPS) system is proposed and two cases of copilot lane change driving scenarios are studied via computer simulation.
Abstract: The challenging issue of “human–machine copilot” opens up a new frontier to enhancing driving safety. However, driver–machine conflicts and uncertain driver/external disturbances are significant problems in cooperative steering systems, which degrade the system's path-tracking ability and reduce driving safety. This paper proposes a novel stochastic game-based shared control framework to model the steering torque interaction between the driver and the intelligent electric power steering (IEPS) system. A six-order driver–vehicle dynamic system, including driver/external uncertainty, is established for path-tracking. Then, the affine linear-quadratic-based path-tracking problem is proposed to model the maneuvers of the driver and IEPS. Particularly, the feedback Nash and Stackelberg frameworks to the affine-quadratic problem are derived by stochastic dynamic programming. Two cases of copilot lane change driving scenarios are studied via computer simulation. The intrinsic relation between the stochastic Nash and Stackelberg strategies is investigated based on the results. And the steering-in-the-loop experiment reveals the potential of the proposed shared control framework in handling driver–IEPS conflicts and uncertain driver/external turbulence. Finally, the copiloting experiments with a human driver further demonstrate the rationality of the game-based pattern between both the agents.
95 citations
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TL;DR: The results show that the complementarity of hydropower and PV power in long-term operations is highly necessary, and considering the uncertainty of stochastic streamflow and PV output simultaneously improves the efficiency of complementary operations.
94 citations
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TL;DR: A two-stage stochastic programming problem for red blood cells that simultaneously considers production, inventory and location decisions and is solved using CPLEX for a real case study from The Hashemite Kingdom of Jordan.
92 citations
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TL;DR: A day-ahead energy management system to decrease the operation cost and increase the reliability of a Microgrid considering a number of challenges for supporting electrical and thermal loads.
Abstract: Using different types of renewable energy sources considering their uncertainties causes numerous challenges for minimizing the operation cost and maximizing the reliability of system. Hence, stochastic programming is an essential tool to consider the system uncertainties. This paper presents a day-ahead energy management system to decrease the operation cost and increase the reliability of a Microgrid considering a number of challenges for supporting electrical and thermal loads. In the proposed method, micro-CHP units, renewable energy sources, auxiliary boiler and energy storage system are all responsible for supplying the electrical and thermal loads. The problem is formulated as a multi-objective optimization problem. Moreover, the influence of considering the electrical energy storage system as a non-ideal battery with charge/discharge efficiency less than 1 is investigated. Also, demand response programs are provided based on load shifting contracts to consumers. A scenario-based approach is used to cover the uncertainties of renewable energy sources, market price and electrical load. Besides, this paper considers both islanding and grid-connected modes of Microgrid and investigates the influence of demand side management on operation cost and reliability in both modes. The capability of the proposed algorithm is analyzed by simulation results of a 3-feeder Microgrid.
90 citations
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TL;DR: A queue model is built to formulate the mobile users’ workload offloading problem and Lyapunov optimization framework is proposed to make trade-off between system offloading utility and queue backlog and results show effectiveness of the Lagrangian optimization offloading method for deterministic WiFi connection and the multi-stage stochastic programming method for random WiFi connection.
Abstract: In order to improve life mobile users' service experience, mobile cloud computing (MCC) is promoted, which can offload these compute-intensive applications to cloud. Although MCC can alleviate the burdens of Smart mobile devices (SMDs), it also aggravates the computing and storage overheads in cloud center and bandwidth overhead on wireless links for applications offloading. Therefore, we should carefully design the offloading policy to decrease these overheads while easing the burdens of SMDs. To this end, we investigate the offloading policy in heterogeneous wireless networks. In this paper, a queue is built to formulate the mobile users' workload offloading problem and Lyapunov optimization is used to make trade-off between the system offloading utility and the queue backlog. First, based on the deterministic WiFi connection, a Lagrangian optimization method is proposed to decide the optimal offloading workloads. Furthermore, considering the random WiFi connection durations, a multi-stage stochastic programming is adopted. The experimental results show the effectiveness of the Lagrangian optimization offloading method for deterministic WiFi connection and the multi-stage stochastic programming method for random WiFi connection.
90 citations
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TL;DR: A mixed integer conic programming (MICP) model to find the optimal type, size, and place of distributed generators (DG) over a multistage planning horizon in radial distribution systems is proposed.
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TL;DR: In this article, the authors identified the tradeoff effects of hydropower and wind power integrated operation by establishing a framework of coupling models, where a martingale model that captures the evolution of forecasting uncertainty was used to generate synthetic scenarios of uncertain load demand.
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TL;DR: The restoration framework is applied to the reduced British electric power system and the results demonstrate the added value of using the stochastic model as opposed to the deterministic model.
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TL;DR: A new heuristic demand response technique for consumption scheduling of appliances in order to decrease peak to average ratio of power demand is introduced, using a hopping scheme to schedule the appliances with flexible schedule without the need to obtain individual consumption of appliances thereby providing a high level of consumers’ confidentiality.
Abstract: A new heuristic demand response technique for consumption scheduling of appliances in order to decrease peak to average ratio of power demand is introduced. The proposed technique uses a hopping scheme to schedule the appliances with flexible schedule without the need to obtain individual consumption of appliances thereby providing a high level of consumers’ confidentiality. The proposed demand response is built on simple mathematical equations that significantly simplify advanced metering infrastructure (AMI) as well as communication requirements. In the stochastic programming, energy consumption scheduler embedded in AMI defines appliances’ consumption vector based on the information vector that is provided in real time by network’s control center. To show the effectiveness of the proposed scheme, its performance in reducing the peak to average and energy retail price is evaluated numerically and compared to idealistic as well as practical benchmarks.
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TL;DR: Three approaches are compared to schedule the charging process of three different electric vehicles fleets at a common charging infrastructure under uncertainty, showing the feasibility to charge different electric vehicle fleets in a car park according to different signals and taking technical restrictions as well as uncertainties into consideration.
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TL;DR: The analysis revealed that the efficiency of waste management system can be maximized by the proper use of these optimization techniques, including the fuzzy-stochastic method, which was increasingly used for dealing with the wastemanagement system uncertainty in recent times.
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TL;DR: The particle swarm optimization (PSO) algorithm is modified for the optimal power sharing among several RESs such as wind, photovoltaic, and a combined heat and power (CHP) plants within a micro-grid framework.
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TL;DR: A new self-scheduling framework for demand response (DR) aggregators, which contributes over the existing models in the following aspects: the model considers the uncertainties posed from consumers and electricity market prices, and applies the information-gap decision theory (IGDT) in the self- scheduling problem.
Abstract: This paper proposes a new self-scheduling framework for demand response (DR) aggregators, which contributes over the existing models in the following aspects. The proposed model considers the uncertainties posed from consumers and electricity market prices. Further, the given model applies the information-gap decision theory (IGDT) in the self-scheduling problem, which guarantees the predefined profit by the aggregator and avoids computational burdens caused by scenario-based methods, such as stochastic programming approaches. The DR aggregator procures DR from two proposed programs, i.e., reward-based DR and time-of-use. Then, the obtained DR is offered into day-ahead and balancing markets. An IGDT-based profit function is proposed, which leads to a bilevel program. The given bilevel model is then transformed into an equivalent single-level model by developing a non-KKT method, which is solved through commercial solvers available in general algebraic modeling system. The feasibility of the problem is studied using a case study with realistic data of electricity markets.
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TL;DR: This article considers how best to strategically transport and route hazardous materials through a bi-modal transportation network (MMTS) consisting of road and rail links and proposes three algorithms, namely Maximum Likelihood Sampling, Sample Average Approximation, and a combination of these two algorithms (MLSAA).
Abstract: This article considers how best to strategically transport and route hazardous materials through a bi-modal transportation network (MMTS) consisting of road and rail links. The placement of transfer yards is also considered as well as the possibility of disruptions at those facilities. For this decision problem a two-stage stochastic programming model is formulated. The objective of the model is to minimize transportation costs and risk. As the model is intractable to solve using traditional techniques, three algorithms are proposed, namely Maximum Likelihood Sampling (MLS), Sample Average Approximation (SAA), and a combination of these two algorithms (MLSAA). Those optimization approaches have been applied to various instances to demonstrate their viability and effectiveness. Numerical testing shows that the MLSAA algorithm is superior and can solve large-scale instances.
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TL;DR: The flexibility and reliability of the optimal resource expansion planning are ensured by means of appropriate constraints incorporated into the proposed planning tool where thermal generation units, ES systems, and DR programs are considered as flexibility resources.
Abstract: This study presents a flexible, reliable, and renewable power system resource planning approach to coordinate generation, transmission, and energy storage (ES) expansion planning in the presence of demand response (DR). The flexibility and reliability of the optimal resource expansion planning are ensured by means of appropriate constraints incorporated into the proposed planning tool where thermal generation units, ES systems, and DR programs are considered as flexibility resources. The proposed planning tool is a mixed-integer non-linear programming (MINLP) problem due to the non-linear and non-convex constraints of AC power flow equations. Accordingly, to linearise the proposed MINLP problem, the AC nodal power balance constraints are linearised by means of the first-order expansion of Taylor's series and the line flow equations are linearised by means of a polygon. Additionally, the stochastic programming is used to characterise the uncertainty of loads, a maximum available power of wind farms, forecasted energy price, and availability/unavailability of generation units and transmission lines by means of a sufficient number of scenarios. The proposed planning tool is implemented on the IEEE 6-bus and the IEEE 30-bus test systems under different conditions. Case studies illustrate the effectiveness of the proposed approach based on both flexibility and reliability criteria.
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TL;DR: The results suggest that the application of the methodology increases peak energy savings up to 17%, scales up solar generation usage up to 23%, and the optimal storage size obtained in the shared community case reduces up to 50%.
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TL;DR: A case study is implemented on a 6-bus power system with a 7-node natural gas system to demonstrate the superiority of the proposed DRO model compared to the existing ARO and data-driven DRO models.
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TL;DR: The results prove that the proposed optimal offering model for the domestic energy management system is more robust than its non-optimal offering model and has a positive effect on the system’s total expected profit.
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TL;DR: The energy hub concept is used to construct a scenario-based model for the optimal scheduling of electrical and thermal resources in a microgrid with integrated electrical and natural gas infrastructures and the results demonstrate the effectiveness of the approach.
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TL;DR: By considering secondary disasters, the sustainable rescue ability can be greatly improved than others only considering primary disasters, and an approximation single-stage stochastic programming by taking the worst-case scenario is proposed.
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TL;DR: Modified Teaching-Learning-Based Optimization (MTLBO) algorithm is used and its performance is evaluated on a modified 33 bus distribution network and results represent that by using MTLBO method, the revenue increases more than 5 percentages in comparison with other optimization methods.
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TL;DR: It is concluded that the stochastic method is efficient for optimal-bidding of GenCos owning CAES and wind units and also for risk-averse GenCos.
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TL;DR: The total operation cost is reduced using the proposed method comparatively to run the program without considering the lower level, and the two-stage stochastic programming to cope with the uncertainty associated with wind power production is applied.
Abstract: This paper proposes a new strategy for an independent system operator (ISO) to trade demand response (DR) with different DR aggregators while considering various operational constraints. The ISO determines the energy scheduling and reserve deployment in a pre-emptive market while setting DR contracts with the DR aggregators. The ISO applies a two-stage stochastic programming to cope with the uncertainty associated with wind power production. DR aggregators’ behavior is modeled through a profit maximization function. Aggregators determine their DR trading shares with ISO and customers through three DR options, including load curtailment, load shifting, and load recovery. A stochastic bilevel problem is formulated, in which in the upper level, the ISO minimizes the total operation cost, and in the lower level, the DR aggregator maximizes the profit. Afterwards, the problem is transferred to a single-level mathematical problem with equilibrium constraints by replacing the lower level program with its Karush–Kuhn–Tucker (KKT) conditions. As a result, the total operation cost is reduced using the proposed method comparatively to run the program without considering the lower level.
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TL;DR: Numerical results confirm the effectiveness of the proposed planning scheme in obtaining an economic investment plan at the presence of several planning alternatives and to promote an environmentally committed electric power distribution network.
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TL;DR: This paper deals with the problem of optimal scheduling of smart residential energy hub (SREH) considering the different uncertain parameters and evaluates the impacts of different values of risk aversion parameter and the utility of the demand response program on the optimal solution of the proposed PV integrated SREH scheduling.
Abstract: Multi-carrier energy systems create new challenges as well as opportunities in future energy systems. One of these challenges is the interaction among different energy hubs' facilities and various operational parameters on the scheduling of the energy hub systems. This paper deals with the problem of optimal scheduling of smart residential energy hub (SREH) considering the different uncertain parameters. The effect of the market prices, demands and solar radiation uncertainties on the SREH scheduling problem is characterised through a risk-constrained two-stage stochastic programming model. The objective of the proposed scheduling problem is to determine the least-cost 24 h operation of the facilities that would cover the cooling, thermal and electrical demands. The Monte Carlo simulation method is applied to model the inaccuracies of solar radiation, energy demands, and electricity market prices. Additionally, a proper scenario-reduction algorithm is employed to reduce the number of scenarios and simulation burden. The proposed approach evaluates the impacts of different values of risk aversion parameter and the utility of the demand response program on the optimal solution of the proposed PV integrated SREH scheduling. Finally, an illustrative example is provided to confirm the efficiency and the applicability of the proposed approach.