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Impact analysis of RES-based DGs with economical considerations in restructured electricity market

ENERGY SOURCES PART B-ECONOMICS PLANNING AND POLICY 2016年第5期11卷 472-479页

作者： Singh, A. K. Parida, S. K. Inst Infrastruct Technol Res & Management IIRAM Dept Elect Engn Ahmadabad Gujarat India Indian Inst Technol Dept Elect Engn Patna Bihar India

The integration of distributed generators (DGs) into the grid is of great importance in improving system reliability. The criterion of minimizing total system cost used previously by many researchers for locating the optimal sites for DGs using optimal power flow (OPF) formulations was considered in this work. Here, three different cost functions are formulated for three kinds of renewable energy source (RES). Three different objectives were considered separately for determining the optimal locations for each kind of RES using the mixed integer non-linear programming (MINLP) method. Having many alternatives with these three objectives, the analytic hierarchy process (AHP) was used to reach a decision over getting the optimal locations for various kinds of RES. The proposed methodology was demonstrated on a 15-node distribution system.

关键词： Analytic hierarchy process distributed generators mixed integer non-linear programming optimal power flow renewable energy sources

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Composite particle algorithm for sustainable integrated dynamic ship routing and scheduling optimization

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COMPUTERS & INDUSTRIAL ENGINEERING 2016年 96卷 201-215页

作者： De, Arijit Mamanduru, Vamsee Krishna Reddy Gunasekaran, Angappa Subramanian, Nachiappan Tiwari, Manoj Kumar Indian Inst Technol Dept Ind & Syst Engn Kharagpur 721302 W Bengal India Univ Massachusetts Dartmouth Dept Decis & Informat Sci 285 Old Westport Rd N Dartmouth MA 02748 USA Univ Nottingham Ningbo China Nottingham Univ Business Sch China 199 Taikang East Rd Ningbo 315100 Zhejiang Peoples R China

Ship routing and scheduling problem is considered to meet the demand for various products in multiple ports within the planning horizon. The ports have restricted operating time, so multiple time windows are taken into account. The problem addresses the operational measures such as speed optimisation and slow steaming for reducing carbon emission. A mixed integer non-linear programming (MINLP) model is presented and it includes the issues pertaining to multiple time horizons, sustainability aspects and varying demand and supply at various ports. The formulation incorporates several real time constraints addressing the multiple time window, varying supply and demand, carbon emission, etc. that conceive a way to represent several complicating scenarios experienced in maritime transportation. Owing to the inherent complexity, such a problem is considered to be NP-Hard in nature and for solutions an effective meta-heuristics named Particle Swarm Optimization-Composite Particle (PSO-CP) is employed. Results obtained from PSO-CP are compared using PSO (Particle Swarm Optimization) and GA (Genetic Algorithm) to prove its superiority. Addition of sustainability constraints leads to a 4-10% variation in the total cost. Results suggest that the carbon emission, fuel cost and fuel consumption constraints can be comfortably added to the mathematical model for encapsulating the sustainability dimensions. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Ship routing Carbon emission mixed integer non-linear programming Maritime transportation Particle Swarm Optimization-Composite Particle

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Coupled optimization of electricity and natural gas systems using augmented Lagrangian and an alternating minimization method

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2016年第0期80卷 202-218页

作者： Biskas, P. N. Kanelakis, N. G. Papamatthaiou, A. Alexandridis, I. Aristotle Univ Thessaloniki Dept Elect Engn Thessaloniki 54124 Greece Hellen Gas Transmiss Syst Operator DESFA Patima Magoulas Attica Greece

A coupled optimization of the electricity and gas systems is presented in this paper. The electricity problem involves a unit commitment with co-optimization of energy and reserves under a power pool, considering all system operational and unit technical constraints. The gas problem involves a medium-scale highly non-convex and non-linear problem structure, which is modeled as a mixed integer non-linear programming model. The decomposition of the overall problem is based on the Augmented Lagrangian method. An iterative process is implemented, coordinating the two interdependent systems using an alternating minimization method, in which the Lagrange multipliers are updated using the subgradient method. The gas problem is solved in two phases in order to avoid numerical instabilities;first, the direction of flow is defined, and then the gas flow is derived in the second phase. The solution algorithm is evaluated using the Greek power and gas system, comprising thirteen gas-fired units and fifty-three gas network nodes. The test results indicate the strong interdependence of the two systems, and demonstrate the efficiency of the presented algorithm in coordinating them. (c) 2016 Elsevier Ltd. All rights reserved.

关键词： mixed integer non-linear programming Augmented Lagrangian method Alternating minimization method Day-ahead electricity market Unit commitment Natural gas network

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Shared and practical approach to conserve utilities in eco-industrial parks

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COMPUTERS & CHEMICAL ENGINEERING 2016年第0期93卷 221-233页

作者： Nair, Sajitha K. Guo, Yingjian Mukherjee, Ushnik Karimi, I. A. Elkamel, Ali Natl Univ Singapore Dept Chem & Biomol Engn 4 Engn Dr 4 Singapore 117585 Singapore Univ Waterloo Dept Chem Engn 200 Univ Ave West Waterloo ON N2L 3G1 Canada

Conserving utilities in an eco-industrial park (EIP) by exploiting the synergistic heating/cooling needs of its inhabitants can have significant economic and environmental benefits. However, a successful implementation of an EIP-wide heat integration involves much more than the simple minimization of utility usage. Like any collaborative endeavour involving independent and diverse profit-making enterprises, an EIP-wide heat integration faces several real and practical challenges such as exchanger locations, stream transports over long distances, etc. In this work, we propose a mixed-integer nonlinear programming model (MINLP) for configuring an EIP-wide multi-enterprise heat exchanger network (HEN). We propose a practical and rational strategy that (1) considers all the major capital and operating costs, and utility savings, (2) selects an optimum HEN location with the highest net present value, (3) uses a third party logistics provider for managing and operating the HEN, and (4) ensures an identical rate of return on investment for all participating enterprises. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： EIP Heat exchanger network synthesis Inter-plant integration mixed integer non-linear programming Optimization

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A location-routing problem for cross-docking networks: A biogeography-based optimization algorithm

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COMPUTERS & INDUSTRIAL ENGINEERING 2016年 102卷 132-146页

作者： Goodarzi, Asefeh Hasani Zegordi, Seyed Hessameddin Tarbiat Modares Univ Fac Ind & Syst Engn POB 14115-143 Tehran Iran

This paper considers a location-routing problem in a distribution network with a set of part suppliers, cross-docking centers and assembly plants known as customers. We develop a mixed integer nonlinear programming formulation for the problem in which the location for establishing the cross-docks is determined while simultaneously a fleet of vehicles are applied to transport goods from suppliers to the assembly plants via two transportation strategies: direct shipment and shipment through cross dock (indirect shipment). In the second strategy, it is possible to have routes between suppliers. Not considering two problems of location and distribution planning simultaneously would result in increasing the costs of supplying parts since the transportation strategy has a huge effect on location of cross docks. In the other words, if some loads can be directly shipped, then this kind of loads should not be taken into account in determining cross-docks location. Thus, a location - routing problem is presented for cross-docking system in this paper. The goal is to determine the location of cross-docks, allocating suppliers to them and routing decisions, so that the location cost and total shipping cost in the network are minimized, considering variable cost of servicing parts passed through cross-docks. The proposed model is NP-hard based on literature. Thus, a metaheuristic algorithm named Biogeography-based optimization (BBO) is utilized to solve the problem. In order to evaluate its efficiency, BBO results are compared with those of PSO, which is a well-known algorithm in the literature. Solving numerical examples for small size problem instances illustrates that the solving approach performs with a negligible gap relative to GAMS, while it performs much better than PSO in most cases in terms of total cost of the network and computational time. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Cross-docking Location-Routing Problem (LRP) Direct shipment Distribution network mixed integer non-linear programming

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Constructing A Shortest Path Overhearing Tree With Maximum Lifetime In WSNs 49

Constructing A Shortest Path Overhearing Tree With Maximum L...

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49th Hawaii International Conference on System Sciences (HICSS)

作者： Alghamdi, Wael Y. Wu, Hui Zheng, Wenguang Kanhere, Salil S. Univ New South Wales Sch Comp Sci & Engn Sydney NSW Australia

ISBN: (纸本)9780769556703

Secure data collection is an important problem in wireless sensor networks. Different approaches have been proposed. One of them is overhearing. We investigate the problem of constructing a shortest path overhearing tree with the maximum lifetime. We propose three approaches. The first one is a polynomial -time heuristic. The second one uses ILP (integer linear programming) to iteratively find a monitoring node and a parent for each sensor node. The last one optimally solves the problem by using MINLP (mixed integer non -linear programming). We have implemented the three approaches using MIDACO solver and MATLAB Intlinprog, and performed extensive simulations using NS2.35. The simulation results show that the average lifetime of all the network instances achieved by the heuristic approach is 85.69% of that achieved by the ILP-based approach and 81.05% of that obtained by the MINLP-based approach, and the performance of the ILP-based approach is almost equivalent to that of the MINLP-based approach.

关键词： Overhearing routing tree network lifetime secure data collection integer linear programming mixed integer non-linear programming

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Integrated planning of natural gas and electricity distribution networks with the presence of distributed natural gas fired generators

Integrated planning of natural gas and electricity distribut...

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IEEE-Power-and-Energy-Society General Meeting (PESGM)

作者： Odetayo, Babatunde MacCormack, John Rosehart, W. D. Zareipour, Hamidreza Univ Calgary Sch Elect & Comp Engn Calgary AB Canada

ISBN: (纸本)9781509041688

A sequential recourse stochastic optimization approach to solving the long-term integrated planning problem of a Natural Gas (NG) distribution system and Natural Gas-fired Distributed Generators (NGDGs) is presented. The NGDG location and sizing problem under uncertain demand is solved in the first stage. The computed location and size of NGDG is employed to compute the NG demand at each node. The deterministic mixed integer non-linear programming NG optimal pipeline route selection problem is solved in the second stage. The solution methodology is illustrated using a simple case study over a long term planning period of 20 years. This work extends previous heuristic based approaches dependent on consideration of limited candidate solutions by employing a two stage recourse stochastic optimization technique and an analytical solution technique for solving the integrated problem. The proposed model allows for planning the future integration of NGDG and NG-pipelines without having to populate a set of expansion options.

关键词： Natural gas fired distributed generation Integrated planning natural gas distribution pipeline mixed integer non-linear programming

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A hybrid artificial immune network for detecting communities in complex networks

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COMPUTING 2015年第5期97卷 483-507页

作者： Karimi-Majd, Amir-Mohsen Fathian, Mohammad Amiri, Babak Iran Univ Sci & Technol Dept Ind Engn Tehran Iran Univ Sydney Sydney NSW 2006 Australia

One of the challenging problems when studying complex networks is the detection of sub-structures, called communities. Network communities emerge as dense parts, while they may have a few relationships to each other. Indeed, communities are latent among a mass of nodes and edges in a sparse network. This characteristic makes the community detection process more difficult. Among community detection approaches, modularity maximization has attracted much attention in recent years. In this paper, modularity density (D value) has been employed to discover real community structures. Due to the inadequacy of previous mathematical models in finding the correct number of communities, this paper first formulates a mixed integer non-linear program to detect communities without any need of prior knowledge about their number. Moreover, the mathematical models often suffer from NP-Hardness. In order to overcome this limitation, a new hybrid artificial immune network (HAIN) has been proposed in this paper. HAIN aims to use a network's properties in an efficient way. To do so, this algorithm employs major components of the pure artificial immune network, hybridized with a well-known heuristic, to provide a powerful and parallel search mechanism. The combination of cloning and affinity maturation components, a strong local search routine, and the presence of network suppression and diversity are the main components. The experimental results on artificial and real-world complex networks illustrate that the proposed community detection algorithm provides a useful paradigm for robustly discovering community structures.

关键词： Complex network Community detection mixed integer non-linear programming Artificial immune network Modularity-based maximization

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Combined cycle unit commitment in a changing electricity market scenario

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2015年 73卷 114-123页

作者： Casolino, G. M. Liuzzi, G. Losi, A. Univ Cassino & Lazio Merid Dipartimento Ingn Elettr & Informaz M Scarano Via G Di Biasio 43 I-03043 Cassino FR Italy CNR Ist Anal Sistemi & Informat A Ruberti I-00185 Rome Italy

For Generation Companies (GENCOs) one of the most relevant issue is the commitment of the units, the scheduling of them over a daily (or longer) time frame, with the aim of obtaining the best profit. It strongly depends on the plant operational generation costs, which depend in turn on the choices taken at the design stage;it follows that design technical choices should also aim at determining the best generation cost structure of generating units with respect to the market opportunities. In the paper the unit commitment (UC) problem has been considered, with highlights on changes in the market scenario. The paper analyzes the relevance of some design choices (structure, size, regulation type) on the economics of the operation of gas-steam combined cycle generating units. To solve the UC problem, a recently proposed method for mixed integer nonlinear programming problems, with the use of a derivative free algorithm to solve the continuous subproblems, has been considered. The results for two GENCOs are reported: one managing a single unit and the other managing three units. Numerical examples show the sensitivity of the UC solutions to the market conditions and to the design choices on the regulation type in the evolving scenario of the Italian Electricity Market. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Unit commitment Oligopolistic electricity markets Gas-steam generating units mixed integer non-linear programming

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Multistage distribution network expansion planning considering the emerging energy storage systems

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ENERGY CONVERSION AND MANAGEMENT 2015年 105卷 938-945页

作者： Saboori, Hedayat Hemmati, Reza Abbasi, Vahid Kermanshah Univ Technol Dept Elect Engn Kermanshah Iran

This paper addresses multistage distribution network expansion planning (DNEP) incorporating energy storage systems (ESSs). The ESSs are utilized to shave the peak demand and to reduce the planning cost. Annual and daily load duration curves are considered to evaluate the impacts of the ESSs on the planning. The proposed planning is carried out based on the AC power flow including active power, reactive power, and network loss. The problem is formulated as a constrained, mixed-integer, and nonlinear programming (MINLP) and solved by using particle swarm optimization (PSO) algorithm. A 11 kV and 30-bus radial distribution network is considered as case study and the typical ESSs are also regarded to install on the network. Simulation results demonstrate the effectiveness and viability of the proposed method to consider the ESSs in DNEP. The results indicate that integrating the ESSs in DNEP reduces the planning cost significantly, as well improves the technical parameters of the network such as bus voltages and line loading. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Distribution network expansion planning Dynamic planning Energy storage system mixed integer non-linear programming Particle swarm optimization

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