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Analysis of emission taxes levying on regional electric power structure adjustment with an inexact optimization model - A case study of Zibo, China

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ENERGY ECONOMICS 2019年 84卷 1页

作者： Guan, Panbo Huang, Guohe Wu, Chuanbao Wang, Linrui Li, Chaoci Wang, Yuanyi North China Elect Power Univ Resources & Environm Res Acad MOE Key Lab Reg Energy & Environm Syst Optimizat Beijing 102206 Peoples R China Univ Regina Fac Engn Environm Syst Engn Program Regina SK S4S 0A2 Canada Shandong Univ Sci & Technol Coll Econ & Management Qingdao 266590 Peoples R China State Power Investment Corp Ltd Mat & Equipment Branch Beijing 100080 Peoples R China

In this study, an inexact two-stage chance-constrained programming (ITSCCP) model was provided for multiple electrical power system supply and demand management in Zibo City under uncertainties. Three scenarios about the electric power structure adjustment, renewable power generation, and the emission taxes were designed. Methods of two-stage stochastic programming (TSP) and inexact chance-constrained programming (ICCP) were incorporated into the developed model to tackle uncertainties in terms of various cost coefficients, decision maker's risk attitude which was described by interval values and probability distributions. Moreover, under the objective of cost minimize, the electrical power generation planning for each terms under different feasibility degrees (violating constraints or available resources situations) can be obtained. The results indicated that higher probability of violating system constraints would increase risk of system, but lower the total cost;the proportion of optimized thermal power generation and imported electricity would decrease, which could promote the energy conservation and emissions reduction in some degree. At the same time, the model results are valuable for decision-makers to tackle the uncertainty of the power generation schemes within a complicated energy system and make a desired compromise between the satisfaction degree of the economic benefits and feasibility degree of constraints. (C) 2019 Elsevier B.V. All rights reserved.

关键词： two-stage stochastic programming Inexact chance-constraint programming Emission taxes Electricity structure adjustment

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Day-ahead stochastic scheduling model considering market transactions in smart grids 20

Day-ahead stochastic scheduling model considering market tra...

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Power Systems Computation Conference (PSCC)

作者： Soares, Joao Lezama, Fernando Canizes, Bruno Ghazvini, M. Ali Fotouhi Vale, Zita Pinto, Tiago Polytech Porto ISEP IPP GECAD Res Grp Intelligent Engn & Comp Adv Innovat Porto Portugal Univ Salamanca BISITE Res Grp Salamanca Spain

ISBN: (纸本)9781910963104

The integration of renewable generation and electric vehicles (EVs) into smart grids poses an additional challenge to the stochastic energy resource management problem due to the uncertainty related to weather forecast and EVs user-behavior. Moreover, when electricity markets are considered, market price variations cannot be disregarded. In this paper, a two-stage stochastic programming approach to schedule the dayahead operation of energy resources in smart grids under uncertainty is presented. A realistic case study is performed using a large-scale scenario with nearly 4 million variables with the goal to minimize expected operation cost of energy aggregators. Three scenarios are analyzed to understand the effect of market transactions and external suppliers on the aggregator model. The results suggest that the market transactions can reduce expected cost, while the external supplier offers risk-free price. In addition, the performance metric shows the superiority of the stochastic approach over an equivalent deterministic model.

关键词： Energy scheduling smart grid uncertainty electric vehicles two-stage stochastic programming

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Strong convexity in risk-averse stochastic programs with complete recourse

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COMPUTATIONAL MANAGEMENT SCIENCE 2018年第3-4期15卷 411-429页

作者： Claus, Matthias Schultz, Ruediger Spuerkel, Kai Univ Duisburg Essen Thea Leymann Str 9 D-45127 Essen Germany

We give sufficient conditions for the expected excess and the mean-upper-semideviation of recourse functions to be strongly convex. This is done in the setting of two-stage stochastic programs with complete linear rec... 详细信息

关键词： two-stage stochastic programming Linear recourse Strong convexity Expected excess Semideviation

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A Risk-averse two-stage stochastic Optimization Model for Water Resources Allocation under Uncertainty

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环境核算与管理（英文） 2018年第1期6卷 71-82页

作者： Ye Xu Sha Li Feng Liu Jinping Qian Yanpeng Cai Guanhui Cheng MOE Key Laboratory of Regional Energy and Environmental Systems Optimization College of Environmental Science and Engineering North China Electric Power University Beijing 102206 China College of Resources and Environmental Sciences Hebei Normal University No.20 Road East.2nd Ring SouthYuhua District Shijiazhuang Hebei 050024 China State Key Laboratory of Water Environment Simulation School of Environment Beijing Normal University Beijing 100875 China Faculty of Engineering University of Regina Regina Saskatchewan Canada S4S 0A2

The water resource shortage has caused intense contradictions among various water users;moreover,unfair and irrational water allocation may result in the economic loss,even the social instability.A risk-averse two-stage stochastic programming model (RATSP) are developed for tackling this *** significant contribution made by the RATSP model is that it concerns the importance of the fmancial risk under each scenario,where the risk is expressed as the probability of occurrence in that benefit generated under each scenario is lower than predefmed target *** can avoid possible suboptimal solutions caused by only considering the maximization of the expected value for all scenarios as an objective ***,the risk measure is incorporated into the objective function,leading a variety of solutions under various weight coefficients and target levels,which is suitable in analyzing the trade-off between the system economy and reliability.A case of water resources management system is used to reflect the applicability and feasibility of this *** obtained results demonstrate that the RATSP model can help decision makers gain in-depth insights into potential system risk,analyze the trade-off between system economy and stability,generate the stable water allocation schemes,and avoid the contradictions among users.

关键词： two-stage stochastic programming Financial risk Trade-off Water allocation Uncertainty

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A two-stage stochastic Optimization Approach for Distribution Network with Load Control 2

A Two-stage Stochastic Optimization Approach for Distributio...

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2nd IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Zhang, Xi Xu, Jian Wei, Congying Wuhan Univ Sch Elect Engn Wuhan Hubei Peoples R China

ISBN: (纸本)9781538685495

With the extensive access of distributed energy resources (DER) and the development of load control technology, the distribution network is gradually becoming more and more initiative, and the economy of power grid can be effectively improved through reasonable scheduling. For this purpose, a two-stage stochastic optimization approach for distribution network with load control is established. The scenario analysis is used to characterize the uncertainty of DER. The first stage of optimization generates a day-ahead power purchase curve and load adjustment curves and calculates the corresponding costs;In the second stage, the mathematic expectation of the difference between the actual purchased electricity in each scenario and the day-ahead purchased electricity is calculated to obtain the reserve costs. The mathematical nature of the model is a large-scale nonlinear mixed integer programming problem, which is difficult to solve. By using the second-order cone (SOC) relaxation technique, the above model can be transformed into a mixed integer second-order cone programming (MISOCP) form, which can be solved effectively. Finally, the simulation analysis of the custom IEEE 14-bus system is carried out to verify the effectiveness of the proposed model.

关键词： distribution network distributed energy resource load control two-stage stochastic programming mixed integer second-order cone programming

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A Day-Ahead Joint Energy and Uncertainty Reserve Market Clearing Model to Manage VRE Uncertainty

A Day-Ahead Joint Energy and Uncertainty Reserve Market Clea...

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IEEE International Conference on Environment and Electrical Engineering (EEEIC) / IEEE Industrial and Commercial Power Systems Europe (ICPS Europe)

作者： Zalzar, Shaghayegh Bompard, Ettore Politecn Torino Dept Energy Turin Italy

ISBN: (纸本)9781538651865

The existing electricity market designs may be not compatible with high penetration of Variable Renewable Energy Sources (VRE) in power systems and need to be improved to address VREs' variability and uncertainty. VREs require flexibility and additional reserve capacities from conventional power plants to offset their real-time deviation from the forecasted output power. Another important issue regarding VRE market participation is the relatively low market clearing prices which in turn threatens the profitability of conventional generators. To address these issues, this paper contributes in modelling a central framework to provide uncertainty reserve service which is simultaneously optimized with energy procurement in day-ahead market. The uncertainty reserve is remunerated through day-ahead reservation price and real-time activation price. The proposed market clearing mechanism is able to provide fair and competitive supplementary revenue stream for conventional generators, while it ensures adequate reserve capacities in an hourly basis, taking into account network constraints, to address VRE variability and uncertainty.

关键词： Uncertainty Reserve Mixed Integer Linear programming Variable Renewable Energy (VRE) two-stage stochastic programming

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Optimization-Based Resource Allocation and Transmission Scheduling for Wireless Networks

Optimization-Based Resource Allocation and Transmission Sche...

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作者： Shiwei Huang University of Manitoba

学位级别：博士

Future wireless communication networks are expected to be more energy-efficient and to provide higher throughput, in order to satisfy the demands for the increasing num- ber of mobile users. Resource allocation and transmission scheduling play more and more important roles in improving the performance of wireless networks, in terms of energy saving, throughput, delay, etc. In this thesis, we consider three networks with different characteristics and objectives, i. e., wireless relay networks for distant trans- missions, dense multi-user coexisting networks, and device-to-device (D2D) assisted mobile edge computing systems for compute-intensive mobile applications. We aim to investigate the key resource allocation and/or transmission scheduling issues in these networks. In particular, i) a transmit power allocation scheme with reduced overheads for amplify-and-forward relay networks is proposed to reduce energy consumption, based on the two-stage stochastic programming method, ii) an analysis framework for buffer-aided decode-and-forward relay networks under time-correlated fading channels is developed and an improved link scheduling/selection policy is presented, through the analyses to two quasi-birth-death Markov chains, iii) an interference-avoidance scheduling scheme for dense multi-user coexisting networks with heterogeneous prior- ities and demands is presented to increase the number of admitted users, on the basis of the column generation method, and iv) a joint optimization of admission control, link scheduling, and resource management for D2D-assisted mobile edge computing is carried out, according to the branch-and-price method. Simulations are performed to verify the effectiveness of the proposed schemes where the performance of networks is shown to be improved significantly.

关键词： Wireless relay networks multi-user coexistence mobile edge computing resource allocation power control link scheduling two-stage stochastic programming quasi-birth-death Markov chains column generation branch-and-price

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Risk-Based two-stage stochastic Optimization Problem of Micro-Grid Operation with Renewables and Incentive-Based Demand Response Programs

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ENERGIES 2018年第3期11卷 610页

作者： Sheikhahmadi, Pouria Mafakheri, Ramyar Bahramara, Salah Damavandi, Maziar Yazdani Catalao, Joao P. S. Univ Kurdistan Dept Elect & Comp Engn Sanandaj 66131 Iran Islamic Azad Univ Sanandaj Branch Dept Elect Engn Sanandaj 66131 Iran Univ Beira Interior C MAST P-6201001 Covilha Portugal INESC TEC P-4200465 Porto Portugal Univ Porto Fac Engn P-4200465 Porto Portugal Univ Lisbon Inst Super Tecn INESC ID P-1049001 Lisbon Portugal

The operation problem of a micro-grid (MG) in grid-connected mode is an optimization one in which the main objective of the MG operator (MGO) is to minimize the operation cost with optimal scheduling of resources and optimal trading energy with the main grid. The MGO can use incentive-based demand response programs (DRPs) to pay an incentive to the consumers to change their demands in the peak hours. Moreover, the MGO forecasts the output power of renewable energy resources (RERs) and models their uncertainties in its problem. In this paper, the operation problem of an MGO is modeled as a risk-based two-stage stochastic optimization problem. To model the uncertainties of RERs, two-stage stochastic programming is considered and conditional value at risk (CVaR) index is used to manage the MGO's risk-level. Moreover, the non-linear economic models of incentive-based DRPs are used by the MGO to change the peak load. The numerical studies are done to investigate the effect of incentive-based DRPs on the operation problem of the MGO. Moreover, to show the effect of the risk-averse parameter on MGO decisions, a sensitivity analysis is carried out.

关键词： demand response programs micro-grid renewable energy resources risk-management two-stage stochastic programming

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A stochastic program approach for path reconstruction oriented sensor location model

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TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2017年 102卷 210-237页

作者： Fu, Chenyi Zhu, Ning Ma, Shoufeng Tianjin Univ Inst Syst Engn Coll Management & Econ Tianjin 300072 Peoples R China

Path flow identification is of particular interest for a number of traffic applications, such as OD demand estimation, link flow inference, and toll freeway revenue management. Optimal positioning of active sensors can help to identify path flows. Due to the stochastic nature of transportation systems, we propose a scenario based two stage stochastic programming framework which considers the uncertainty of the link-path matrix. The first stage model aims to minimize the total traffic sensor installation cost and the expected penalty for uncovered and undifferentiated paths. The second stage model attempts to minimize uncovered and undifferentiated paths for a given sensor location pattern and a specific scenario. In addition, a mean risk measure is also incorporated into the two stage stochastic programming framework, and consequently a mean risk two stage stochastic programming model is proposed. Both models have the same structure, where the first stage and second stage decision variables are binary. The second stage decision variable can be relaxed to a continuous variable without changing the nature of the model. To solve the two stochastic programming models, a branch and bound based integer L-shaped algorithm is presented. Finite steps convergence is guaranteed for the algorithm. To handle the problem with a large number of scenarios, a sampling technique is introduced, and the confidence bound is analyzed with respect to the scenario size. Extensive numerical experiments are conducted to verify the effectiveness of the proposed models and algorithm. The most important numerical results are as follows: (i) the stochastic programming framework is capable of capturing the reality more efficiently and accurately, (ii) the path differentiation factor is more critical than the path coverage factor in determining the sensor placement pattern, and (iii) in the partial parameter setting case, the mean risk based stochastic programming model results in a significantly

关键词： Path reconstruction Traffic sensor location two-stage stochastic programming Branch and bound based integer L-shaped algorithm Confidence bound

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Column generation strategies and decomposition approaches for the two-stage stochastic multiple knapsack problem

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COMPUTERS & OPERATIONS RESEARCH 2017年第Jul.期83卷 125-139页

作者： Tonissen, D. D. van den Akker, J. M. Hoogeveen, J. A. Eindhoven Univ Technol Sch Ind Engn POB 513 NL-5600 MB Eindhoven Netherlands Univ Utrecht Dept Informat & Comp Sci Princetonpl 5 NL-3584 CC Utrecht Netherlands

Many problems can be formulated by variants of knapsack problems. However, such models are deterministic, while many real-life problems include some kind of uncertainty. Therefore, it is worthwhile to develop and test knapsack models that can deal with disturbances. In this paper, we consider a two stage stochastic multiple knapsack problem. Here, we have a multiple knapsack problem together with a set of possible disturbances. For each disturbance, or scenario, we know its probability of occurrence and the resulting reduction in the sizes of the knapsacks. For each knapsack we decide in the first stage which items we take with us, and when a disturbance occurs we are allowed to remove items from the corresponding knapsack. Our goal is to find a solution where the expected revenue is maximized. We use branch-and-price to solve this problem. We present and compare two solution approaches: the separate recovery decomposition (SRD) and the combined recovery decomposition (CRD). We prove that the LP-relaxation of the CRD is stronger than the LP-relaxation of the SRD. Furthermore, we investigate numerous column generation strategies and methods to create additional columns outside the pricing problem. These strategies reduce the solution time significantly. To the best of our knowledge, there is no other paper that investigates such strategies so thoroughly. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： two-stage stochastic programming Recoverable robustness Column generation Branch-and-price Multiple knapsack problem

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