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Optimal structural design of residential power and heat supply devices in consideration of operational and capital recovery constraints

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APPLIED ENERGY 2016年 163卷 118-133页

作者： Wakui, Tetsuya Kawayoshi, Hiroki Yokoyama, Ryohei Osaka Prefecture Univ Dept Mech Engn Naka Ku 1-1 Gakuen Cho Sakai Osaka 5998531 Japan

An optimal structural design model of residential power and heat supply devices (R-PHDs), including cogeneration and heat pump units, was developed by considering their operational and capital recovery constraints. The structure of R-PHDs with peripheral devices, sizes of energy storage devices, and their multi-period operation were determined so as to minimize annual primary energy consumption. In addition to various operational constraints for a cogeneration unit and a battery unit, an operable hours constraint and daily selection of outlet water temperature were newly formulated for heat pump units. The capital recovery by reducing energy cost was also considered in order to select R-PHDs that have high energy-saving effects but are presently expensive. The developed model took the form of a mixed-integer linear programming problem. The model was applied to the structural design of R-PHDs, whose candidates are three types of cogeneration units (gas engine, polymer electrolyte fuel cell, and solid oxide fuel cell), heat pump unit, storage tank, battery unit, and peripheral devices. The result revealed a trade-off between energy savings and initial cost in the optimal structures. A sensitivity analysis of the initial costs of expensive devices was also carried out to clarify their initial cost targets. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Cogeneration Heat pump Battery Structural design Optimization mixed-integer linear programming

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Optimal dynamic allocation of mobile plants to monetize associated or stranded natural gas, part II: Dealing with uncertainty

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ENERGY 2016年 96卷 461-467页

作者： Tan, Siah Hong Barton, Paul I. MIT Proc Syst Engn Lab 77 Massachusetts Ave Cambridge MA 02139 USA

Using the Bakken shale play as a case study, the previous part of this two-part series demonstrated how small-scale mobile plants could be used to monetize associated or stranded gas effectively. Here, we address the issue of uncertainty in future supply, demand and price conditions. To this end, we modified our multi-period optimization framework to a stochastic programming framework to account for various scenarios with different parameter realizations in the future. The maximum ENPV (expected net present value) obtained was $2.01 billion, higher than the NPV obtained in the previous part. In addition, the value of the stochastic solution was 0.11% of the optimal ENPV, indicating that the flexible nature of mobile plants affords them a great advantage when dealing with uncertainty. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Natural gas monetization Mobile modular plants mixed-integer linear programming Two-stage stochastic programming Parametric uncertainty Bakken shale play

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Assessing solution quality and computational performance in the hydro unit commitment problem considering different mathematical programming approaches

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ELECTRIC POWER SYSTEMS RESEARCH 2016年 136卷 212-222页

作者： Finardi, E. C. Takigawa, F. Y. K. Brito, B. H. Univ Fed Santa Catarina Elect Syst Planning Res Lab BR-88040900 Florianopolis SC Brazil Fed Inst Santa Catarina BR-88020300 Florianopolis SC Brazil Fed Inst Tocantins BR-77020450 Palmas Brazil

This paper presents a comparative analysis of different mathematical programming approaches for optimizing the hydro unit commitment (HUC) problem with cascaded plants, multiple generating units, and a head-dependent hydropower model. Regarding the HUC problem related to this paper, the objective is to minimize the cascade outflow while satisfying all constraints, including a power target for each plant, in a day-ahead planning horizon. The decision variables are the on/off status of the units and the respective generation levels. Rigorously, the HUC is a mixed-integer nonlinear programming (MINLP) problem, and several strategies can be used to compute near-optimal solutions. In this paper, we are interested in accessing the solution quality, as well as the computational performance when the HUC problem is solved using the following mathematical programming approaches: (i) the Lagrangian relaxation that represents a decomposition technique that exploits the HUC modeling structure, (ii) a MINLP solver that can handle the size and the non-concavity of the problem, and (iii) a mixed-integer linear programming (MILP) approach obtained by means of the hydropower model linearization. To perform the proposed analysis, numerical results are presented related to a real hydro system with eight cascaded reservoirs and 29 generating units. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Hydro unit commitment Lagrangian relaxation mixed-integer nonlinear programming mixed-integer linear programming Hydropower model

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Strategic CBDR bidding considering FTR and wind power

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IET GENERATION TRANSMISSION & DISTRIBUTION 2016年第10期10卷 2464-2474页

作者： Fang, Xin Li, Fangxing Hu, Qinran Wei, Yanli Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN 37996 USA Harvard Univ Sch Engn & Appl Sci Cambridge MA 02138 USA Southern Calif Edison Power Supply Dept Rosemead CA 91770 USA

The independent system operators (ISOs) usually offer financial transmission right (FTR) as a financial instrument for electricity market participants to hedge against the transmission congestion cost. Meanwhile, the development of demand response (DR) provides load serving entities (LSEs) opportunities to perform coupon based demand response (CBDR) programs, and thus LSEs can behave as strategic bidders in the whole-sale market by adjusting its demand level. In the existing approaches for modelling CBDR, the potential impact of FTR which leads LSEs to obtain the congestion compensation under a high load level is overlooked. Therefore, this study proposes a comprehensive strategic CBDR model in which the LSE's profit is maximised by providing CBDR to customers and the congestion compensation from the LSE bidder's FTR holding is also considered. The proposed model is formulated as a bi-level optimisation problem with the LSE's net revenue maximisation as the upper level and the ISO's economic dispatch considering wind power uncertainty as the lower level problem. The bi-level model is addressed with mathematic program with equilibrium constraints technique and mixed-integer linear programming, which can be solved using available optimisation software tools. In addition, the case studies of an illustrative two bus system, the PJM 5-bus system, and IEEE 39-bus system verify the proposed method.

关键词： wind power plants power markets power transmission economics costing demand side management integer programming linear programming power generation economics power generation dispatch strategic CBDR bidding wind power independent system operators ISO financial transmission right financial instrument electricity market participant transmission congestion cost demand response development DR development load serving entities LSE opportunities coupon-based demand response program CBDR program strategic bidders whole-sale market congestion compensation comprehensive strategic CBDR model LSE bidder FTR holding bi-level optimisation problem LSE net revenue maximisation ISO economic dispatch wind power uncertainty mathematic program equilibrium constraint technique mixed-integer linear programming PJM 5-bus system IEEE 39-bus system

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Nested general variable neighborhood search for the periodic maintenance problem

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2016年第2期252卷 385-396页

作者： Todosijevic, Raca Benmansour, Rachid Hanafi, Said Mladenovic, Nenad Artiba, Abdelhakim Univ Valenciennes & Hainaut Cambresis LAMIH UMR 8201 CNRS Valenciennes France Serbian Acad Arts & Sci Math Inst Kneza Mihaila 36 Belgrade 11001 Serbia

In this paper we study the periodic maintenance problem: given a set of m machines and a horizon of T periods, find indefinitely repeating itself maintenance schedule such that at most one machine can be serviced at each period. In addition, all the machines must be serviced at least once for any cycle. In each period the machine i generates a servicing cost b(i) or an operating cost which depends on the last period in which i was serviced. The operating cost of each machine i in a period equals a(i) times the number of periods since the last servicing of that machine. The main objective is to find a cyclic maintenance schedule of a periodicity T that minimizes total cost. To solve this problem we propose a new mixed integer programming formulation and a new heuristic method based on general Variable neighborhood search called Nested general variable neighborhood search. The performance of this heuristic is shown through an extensive experimentation on a diverse set of problem instances. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Scheduling Preventive maintenance mixed-integer linear programming Variable neighborhood search Nested general VNS

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Robust Scheduling of Smart Appliances in Active Apartments With User Behavior Uncertainty

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2016年第1期13卷 247-259页

作者： Paridari, Kaveh Parisio, Alessandra Sandberg, Henrik Johansson, Karl Henrik KTH Royal Inst Technol ACCESS Linnaeus Ctr S-10044 Stockholm Sweden KTH Royal Inst Technol Sch Elect Engn Dept Automat Control S-10044 Stockholm Sweden Univ Manchester Sch Elect & Elect Engn Elect Energy & Power Syst Grp Manchester M13 9PL Lancs England

In this paper, we propose a robust approach for scheduling of smart appliances and electrical energy storages (EESs) in active apartments with the aim of reducing both the electricity bill and the CO2 emissions. The proposed robust formulation takes the user behavior uncertainty into account so that the optimal appliances schedule is less sensitive to unpredictable changes in user preferences. The user behavior uncertainty is modeled as uncertainty in the cost function coefficients. In order to reduce the level of conservativeness of the robust solution, we introduce a parameter allowing to achieve a trade-off between the price of robustness and the protection against uncertainty. Mathematically, the robust scheduling problem is posed as a multi-objective mixed integer linear programming (MILP), which is solved by using standard algorithms. The numerical results show effectiveness of the proposed approach to increase both the electricity bill and CO2 emissions savings, in the presence of user behavior uncertainties. Mathematical insights into the robust formulation are illustrated and the sensitivity of the optimum cost in the presence of uncertainties is investigated. Although home appliances and EESs are considered in this work, we point out that the proposed scheduling framework is generally applicable to many use cases, e.g., charging and discharging of electrical vehicles in an effective way. In addition, it is applicable to various scenarios considering different uncertainty sources, different storage technologies and generic programmable electrical loads, as well as different optimization criteria.

关键词： Demand response mixed-integer linear programming multi-objective robust optimization robust scheduling of smart appliances user behavior uncertainty

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Hydro Unit Commitment With a Head-Sensitive Reservoir and Multiple Vibration Zones Using MILP

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IEEE TRANSACTIONS ON POWER SYSTEMS 2016年第6期31卷 4842-4852页

作者： Cheng, Chuntian Wang, Jiayang Wu, Xinyu Dalian Univ Technol Inst Hydropower & Hydroinformat Dalian 116024 Peoples R China

A batch of huge hydropower plants with multiple vibration zones have been developed in China. These vibration zones varying with net head, i.e., head-sensitive, seriously challenge the hydro unit commitment (HUC). This paper presents a MILP model for HUC with a head-sensitive reservoir and multiple vibration zones. The focus of the study is mainly on two net head related constraints, namely the unit performance curves and multiple vibration zones. These constraints are linearized through the use of binary 0-1 integer variables. The practical behavior of the MILP model is demonstrated using real-world case studies. The results demonstrate that the proposed model is computationally efficient and suitable for large hydropower plants with multiple vibration zones, showing its potential practicability and validity for solving the HUC problem.

关键词： Hydro unit commitment hydropower head effect mixed-integer linear programming multiple vibration zones

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An adjustable robust optimization approach to scheduling of continuous industrial processes providing interruptible load

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COMPUTERS & CHEMICAL ENGINEERING 2016年 86卷 106-119页

作者： Zhang, Qi Morari, Michael F. Grossmann, Ignacio E. Sundaramoorthy, Arul Pinto, Jose M. Carnegie Mellon Univ Dept Chem Engn Ctr Adv Proc Decis Making Pittsburgh PA 15213 USA Swiss Fed Inst Technol Zurich ETHZ Dept Chem & Bioengn CH-8092 Zurich Switzerland Praxair Inc Business & Supply Chain Optimizat R&D Tonawanda NY 14150 USA Praxair Inc Business & Supply Chain Optimizat R&D Danbury CT 06810 USA

To ensure the stability of the power grid, backup capacities are called upon when electricity supply does not meet demand due to unexpected changes in the grid. As part of the demand response efforts in recent years, large electricity consumers are encouraged by financial incentives to provide such operating reserve in the form of load reduction capacities (interruptible load). However, a major challenge lies in the uncertainty that one does not know in advance when load reduction will be requested. In this work, we develop a scheduling model for continuous industrial processes providing interruptible load. An adjustable robust optimization approach, which incorporates recourse decisions using linear decision rules, is applied to model the uncertainty. The proposed model is applied to an illustrative example as well as a real-world air separation case. The results show the benefits from selling interruptible load and the value of considering recourse in the decision-making. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Production scheduling Demand response Interruptible load Adjustable robust optimization mixed-integer linear programming

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Optimal Restoration of Distribution Systems with Active Participation of Volatile Renewable Generators

Optimal Restoration of Distribution Systems with Active Part...

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IEEE Manchester PowerTech

作者： Raoofsheibani, Davood Hinkel, Philipp Ostermann, Martin Wellssow, Wolfram H. Nemati, Mohsen Neisius, Hans-Theo Univ Kaiserslautern Inst Energy Syst & Energy Management Kaiserslautern Germany Siemens AG Digital Grid Software & Solut Energy Management Div Nurnberg Germany

ISBN: (纸本)9781509042371

A strategy for a secure power system restoration (PSR) at distribution levels with active participation of volatile but remotely-controllable (RC) renewable energy source (RES) generators is proposed. It is based on an Awareness Tool as well as a Decision-Support Tool to assist distribution system operators (DSO) in their decision-making process during PSR. The Awareness Tool determines the active power peaks at and after the instant of re-energization for each and every substation of a distribution grid where loads and volatile RES generations are fully mixed. The Decision-Support Tool utilizes the mixed-integer linear programming (MILP) technique to determine network clusters with the help of the Awareness Tool results. The objective is to create different load-dominant areas (load clusters) at the DSO level which consume smooth active powers from the transmission levels for a specific forward-looking time horizon.

关键词： Control center applications distribution system operator mixed-integer linear programming power system restoration renewable energy source

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Unit Commitment With Continuous-Time Generation and Ramping Trajectory Models

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IEEE TRANSACTIONS ON POWER SYSTEMS 2016年第4期31卷 3169-3178页

作者： Parvania, Masood Scaglione, Anna Univ Utah Dept Elect & Comp Engn Salt Lake City UT 84112 USA Arizona State Univ Sch Elect Comp & Energy Engn Tempe AZ 85287 USA

There is increasing evidence of shortage of ramping resources in the real-time operation of power systems. To explain and remedy this problem systematically, in this paper we take a novel look at the way the day-ahead unit commitment (UC) problem represents the information about load, generation and ramping constraints. We specifically investigate the approximation error made in mapping of the original problem, that would decide the continuous-time generation and ramping trajectories of the committed generating units, onto the discrete-time problem that is solved in practice. We first show that current practice amounts to approximating the trajectories with linear splines. We then offer a different representation through cubic splines that provides physically feasible schedules and increases the accuracy of the continuous-time generation and ramping trajectories by capturing sub-hourly variations and ramping of load in the day-ahead power system operation. The corresponding day-ahead UC model is formulated as an instance of mixed-integer linear programming (MILP), with the same number of binary variables as the traditional UC formulation. Numerical simulation over real load data from the California ISO demonstrate that the proposed UC model reduces the total day-ahead and real-time operation cost, and the number of events of ramping scarcity in the real-time operations.

关键词： Continuous-time function space generation trajectory mixed-integer linear programming ramping trajectory unit commitment

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