This paper introduces a novel modelling of the economics of energy storages providing frequency containment reserves. The focus lies both on the practical operation of day-ahead markets and the stochastic and autocorr...
详细信息
ISBN:
(纸本)9781467384636
This paper introduces a novel modelling of the economics of energy storages providing frequency containment reserves. The focus lies both on the practical operation of day-ahead markets and the stochastic and autocorrelation characteristics of the grid frequency as they are practical concerns for any energy storage operator. Based on yearly series of grid-frequency measurements we put in light seasonal trends in the grid frequency. After having removed these trends we build an ARMA-GARCH model with fat-tail feature to explain the remaining autocorrelation. The overall model can be used to generate scenarios of 15-minute time-step frequency deviations and we explain how these scenarios can be used along with stochastic programming to dispatch and compute the revenues of a storage providing frequency containment reserves. By considering two different cases, battery storage and variable-speed pumped-storage, the paper emphasizes the importance of the technical characteristics of energy storages providing frequency containment reserves.
The utility system is a crucial power source for chemical production processes, and the sustainable utility system is one of the key research topics in the field of energy and chemical engineering. To reduce the carbo...
详细信息
The utility system is a crucial power source for chemical production processes, and the sustainable utility system is one of the key research topics in the field of energy and chemical engineering. To reduce the carbon emissions of the system, this study integrates the utility system with renewable energy and energy storage devices, transforming it into a sustainable utility system. To address the impact of multiscale uncertainties in renewable energy supply and steam demand on system decision optimization, this study develops a two-stage hybrid interval-stochastic programming method using stochastic intervals to model multiscale uncertainties to enhance modeling flexibility and reduce computational time. In the first stage, the capacities of renewable energy and storage systems are planned. The second stage involves solving an optimization problem under the uncertainties of renewable energy. The stochastic behavior of wind speed, solar irradiance, and steam demand is captured using scenario trees in the stochastic programming framework. In constructing the scenario tree, uncertainties are modeled by combining stochastic intervals. A risk coefficient is defined for the approximate representation of stochastic intervals to address the challenge of solving interval uncertainties while ensuring the flexibility of steam and power generation among the utility system, renewable energy system, and storage devices. Finally, a case study of a utility system in an actual ethylene chemical process validated the economic and environmental benefits of sustainable retrofitting, as well as the effectiveness of the proposed method in handling uncertainties. The optimization results indicate that the proposed model reduces carbon emissions by 5.2%, and the proposed method decreases computational time by 91% compared to stochastic programming.
In this paper, based on the stochastic optimization theory, we add synthesizing effect constraint condition to stochastic programming model. Through synthesizing effect constraint of expectation and variance, we estab...
详细信息
ISBN:
(纸本)9781424447053
In this paper, based on the stochastic optimization theory, we add synthesizing effect constraint condition to stochastic programming model. Through synthesizing effect constraint of expectation and variance, we establish a kind of stochastic programming model which has guiding significance. We also show that under certain condition, this new model is a convex programming, and prove that if the independent random variables are the normal distribution, the stochastic programming model with synthesizing effect function constraints is equivalent to chance constrained programming model. Last, we get the algorithm of this model.
A stochastic programming model of the operation of energy plants with the introduction of photovoltaic generation and a storage battery is developed. The uncertainty of the output of the photovoltaic generation is rep...
详细信息
ISBN:
(数字)9783030148126
ISBN:
(纸本)9783030148119;9783030148126
A stochastic programming model of the operation of energy plants with the introduction of photovoltaic generation and a storage battery is developed. The uncertainty of the output of the photovoltaic generation is represented by a set of discrete scenarios, and the expected value of the operation cost is minimized. The effectiveness of the stochastic programming model by comparing it with the deterministic model is shown. As an economic evaluation, the recovery period for the initial investment of photovoltaic generation and storage battery is also shown.
This paper proposes a solution approach for the optimal capacity planning problem in multi-area power systems in the presence of randomness in the availability of additional units. The problem is formulated as a two-s...
详细信息
ISBN:
(纸本)9781424412969
This paper proposes a solution approach for the optimal capacity planning problem in multi-area power systems in the presence of randomness in the availability of additional units. The problem is formulated as a two-stage recourse model in the mixed-integer stochastic programming framework. The uncertainties in area generation, transmission fines, and load are incorporated in the model and characterized by their discrete probability distributions. Reliability index used in this analysis is expected unserved energy as this index integrates duration and magnitude of load loss. The objective function is to minimize expansion cost in the first stage and, at the same time, to minimize operation and expected unserved energy costs in the second stage. The first stage decision variables on the number of additional units are represented by binary variables to allow individual unit availability considerations. The solution algorithm utilizes the L-shaped method.
This study is concerned with the determination of an optimal appointment schedule in an outpatient-inpatient hospital system where the inpatient exams can be cancelled based on certain rules while the outpatient exams...
详细信息
This study is concerned with the determination of an optimal appointment schedule in an outpatient-inpatient hospital system where the inpatient exams can be cancelled based on certain rules while the outpatient exams cannot be cancelled. stochastic programming models were formulated and solved to tackle the stochasticity in the procedure durations and patient arrival patterns. The first model, a two-stage stochastic programming model, is formulated to optimize the slot size. The second model further optimizes the inpatient block (IPB) placement and slot size simultaneously. A computational method is developed to solve the second optimization problem. A case study is conducted using the data from Magnetic Resonance Imaging (MRI) centers of Lahey Hospital and Medical Center (LHMC). The current schedule and the schedules obtained from the optimization models are evaluated and compared using simulation based on FlexSim Healthcare. Results indicate that the overall weighted cost can be reduced by 11.6% by optimizing the slot size and can be further reduced by an additional 12.6% by optimizing slot size and IPB placement simultaneously. Three commonly used sequencing rules (IPBEG, OPBEG, and a variant of ALTER rule) were also evaluated. The results showed that when optimization tools are not available, ALTER variant which evenly distributes the IPBs across the day has the best performance. Sensitivity analysis of weights for patient waiting time, machine idle time and exam cancellations further supports the superiority of ALTER variant sequencing rules compared to the other sequencing methods. A Pareto frontier was also developed and presented between patient waiting time and machine idle time to enable medical centers with different priorities to obtain solutions that accurately reflect their respective optimal tradeoffs. An extended optimization model was also developed to incorporate the emergency patient arrivals. The optimal schedules from the extended model show on
In this paper, by analyzing the essential characteristic of stochastic programming and the deficiencies of the existing methods, we propose the concept of synthesizing effect function for processing the objective func...
详细信息
ISBN:
(纸本)9780769538044
In this paper, by analyzing the essential characteristic of stochastic programming and the deficiencies of the existing methods, we propose the concept of synthesizing effect function for processing the objective function and constraints, and further we give an axiomatic system for synthesizing effect function. Finally, we establish a general solution model based on synthesizing effect function for stochastic programming problem, and analyze the model through an example. All the results indicate that our method not only includes the existing methods for stochastic programming, but also effectively merge the decision preferences into the solution, so it can be widely used in many fields such as complicated system optimization and artificial intelligence etc.
Transmission expansion planning (TEP) is helping the system operator to decide the optimal solution for building new lines and in the same time to increase the reliability and safety of the existing power system. The ...
详细信息
ISBN:
(纸本)9781538639436
Transmission expansion planning (TEP) is helping the system operator to decide the optimal solution for building new lines and in the same time to increase the reliability and safety of the existing power system. The proposed problem is a mixed-integer nonlinear programming problem (MINLP) and it is solved using stochastic programming. stochastic programming is applied when uncertain environment occurs, in this case the uncertain environment refers to the production of renewable energy sources (RES) and its dependence on the short-term weather conditions. stochastic Optimization weights all scenarios considered in this paper in order to obtain an expected total cost. The expected total cost includes the cost associated with the construction of new transmission lines, generation cost and load shedding cost.
When supply chain networks become more complex through the application of modern trends such as outsourcing and global marketing, supply chains become more uncertain. Supply chain planning under uncertainty is a chall...
详细信息
ISBN:
(纸本)9783319684963;9783319684956
When supply chain networks become more complex through the application of modern trends such as outsourcing and global marketing, supply chains become more uncertain. Supply chain planning under uncertainty is a challenge for decision makers. Without considering uncertainties in supply chain planning, global supply chains may suffer enormous economic costs. When probability distributions for uncertain parameters can be estimated, stochastic programming can be used for capturing the characteristics of uncertainties and generating flexible production and transportation plans for global supply chains. This paper presents an outline on how to use stochastic programming for decision support under uncertainty. This includes a high level exposition of how to quantify uncertainties, develop stochastic programming models, generate representative scenarios, apply algorithms for model solving, undertake experimental design and present computational results. Through exemplifying supply chain planning and decision making under uncertainty by using stochastic programming, this paper aims to provide a valuable reference for future research in this area.
Model predictive thrust control (MPTC) is one of the most effective approaches for linear induction motor (LIM) drive system. It can achieve the optimization of multiple objectives. However, the process of tuning the ...
详细信息
ISBN:
(纸本)9781728164014
Model predictive thrust control (MPTC) is one of the most effective approaches for linear induction motor (LIM) drive system. It can achieve the optimization of multiple objectives. However, the process of tuning the weighting factors in the objective function is the main drawback of MPTC. It greatly increases the computation burden. In this paper, the tuning process of weighting factors is regarded as a random sampling process. Then, a novel weighting factor optimization method based on the stochastic programming technique is proposed to select the suitable control action for LIM. It will optimize with flux and thrust together to avoid the adjustment of weighting factor. In this paper, the optimal model can be solved by Monte Carlo simulation. At last, the simulation results have shown better dynamic and steady state performance of the proposed method.
暂无评论