Operational Strategy of a Cogeneration System Under a Complex Utility Rate Structure

1996 ◽  
Vol 118 (4) ◽  
pp. 256-262 ◽  
Author(s):  
R. Yokoyama ◽  
K. Ito
Keyword(s):  
Energy Demand ◽  
Large Scale ◽  
Numerical Study ◽  
Mixed Integer ◽  
Planning Problem ◽  
Total Utility ◽  
Operational Strategy ◽  
Rate Structure ◽  
Cogeneration System ◽  
Utility Rate

An operational planning problem for a cogeneration system is discussed under a complex utility rate structure which imposes demand charges due to total utility consumption over a specified period as well as demand and energy charges due to hourly utility consumption. Operational strategy of constituent equipment and contract demands for total utility consumption are assessed so as to minimize the operational cost over the period subject to energy demand requirement. This problem is formulated as a large-scale mixed-integer linear programming (MILP) one, and it is solved efficiently by a revised decomposition method for MILP problems with block angular structure. Through a numerical study on a gas engine-driven cogeneration system installed in a hotel or an office building, the effect of rate structure on operational strategy is clarified.

Optimal Operation of a Gogeneration Plant in Consideration of Equipment Startup/Shutdown Cost

1999 ◽  
Vol 121 (4) ◽  
pp. 254-261 ◽  
Author(s):  
R. Yokoyama ◽  
K. Ito
Keyword(s):  
Energy Supply ◽  
Large Scale ◽  
Numerical Study ◽  
District Heating ◽  
Computation Time ◽  
Optimal Operation ◽  
Operational Planning ◽  
Mixed Integer ◽  
Planning Problem ◽  
Operational Strategy

A rational method of determining the operational strategy of energy supply plants in consideration of equipment startup/shutdown cost is proposed. The operational planning problem is formulated as a large-scale mixed-integer linear programming one, in which on/off status and energy flow rates of equipment are determined so as to minimize the sum of energy supply and startup/shutdown costs over the period considered. By utilizing a special structure of the problem, an algorithm of solving the problem efficiently is proposed. Through a numerical study on the daily operational planning of a gas turbine cogeneration plant for district heating and cooling, the effectiveness of the proposed algorithm, is ascertained in terms of computation time, and the influence of equipment startup/shutdown cost on the operational strategy and cost is clarified.

Optimal Operational Planning of Cogeneration Systems With Thermal Storage by the Decomposition Method

1995 ◽  
Vol 117 (4) ◽  
pp. 337-342 ◽  
Author(s):  
R. Yokoyama ◽  
K. Ito
Keyword(s):  
Energy Demand ◽  
Decomposition Method ◽  
Large Scale ◽  
Numerical Study ◽  
Thermal Storage ◽  
Operational Planning ◽  
Mixed Integer ◽  
Operational Cost ◽  
Cogeneration System ◽  
Method Effects

An optimal operational planning method is proposed for cogeneration systems with thermal storage. The daily operational strategy of constituent equipment is determined so as to minimize the daily operational cost subject to the energy demand requirement. This optimization problem is formulated as a large-scale mixed-integer linear programming one, and it is solved by means of the decomposition method. Effects of thermal storage on the operation of cogeneration systems are examined through a numerical study on a gas engine-driven cogeneration system installed in a hotel. This method is a useful tool for evaluating the economic and energy-saving properties of cogeneration systems with thermal storage.

scholarly journals Optimal Operation of a Gas Turbine Cogeneration Plant in Consideration of Equipment Startup/Shutdown Cost

1997 ◽  
Author(s):  
Ryohei Yokoyama ◽  
Koichi Ito
Keyword(s):  
Gas Turbine ◽  
Energy Supply ◽  
Large Scale ◽  
Numerical Study ◽  
Optimal Operation ◽  
Operational Planning ◽  
Mixed Integer ◽  
Planning Problem ◽  
Cogeneration Plant ◽  
Operational Strategy

A rational method of determining the operational strategy of energy supply plants in consideration of equipment startup/shutdown cost is proposed. The operational planning problem is formulated as a large-scale mixed-integer linear programming one, in which on/off status and energy flow rates of equipment are determined so as to minimize the sum of energy supply and startup/shutdown costs over the period considered. By utilizing a special structure of the problem, an algorithm of solving the problem efficiently is proposed. Through a numerical study on the daily operational planning of a gas turbine cogeneration plant for district heating and cooling, the effectiveness of the proposed algorithm is ascertained in terms of computation time, and the influence of equipment startup/shutdown cost on the operational strategy and cost is clarified.

Optimal Multistage Expansion Planning of a Gas Turbine Cogeneration Plant

1996 ◽  
Vol 118 (4) ◽  
pp. 803-809 ◽  
Author(s):  
R. Yokoyama ◽  
K. Ito ◽  
Y. Matsumoto
Keyword(s):  
Gas Turbine ◽  
Energy Demand ◽  
Numerical Study ◽  
District Heating ◽  
Development Project ◽  
Planning Problem ◽  
Optimization Approach ◽  
Cogeneration Plant ◽  
Expansion Planning ◽  
Heating And Cooling

A multistage expansion planning problem is discussed concerning a gas turbine cogeneration plant for district heating and cooling using an optimization approach. An optimal sizing method for single-stage planning proposed by the authors is extended to this case. Equipment capacities and utility maximum demands at each expansion stage are determined so as to minimize the levelized annual total cost subject to increasing energy demands. A numerical study on a simple-cycle gas turbine cogeneration plant to be installed in a district development project clarifies the relationship between optimal expansion planning and energy demand trend, and shows the effectiveness of the proposed method.

Robust Optimal Operation of a Gas Turbine Cogeneration Plant Under Uncertain Energy Demands

2014 ◽  
Vol 137 (2) ◽  
Author(s):  
Ryohei Yokoyama ◽  
Masashi Ohkura ◽  
Tetsuya Wakui
Keyword(s):  
Gas Turbine ◽  
Energy Supply ◽  
Numerical Study ◽  
Optimization Method ◽  
Optimal Operation ◽  
Mixed Integer ◽  
Cogeneration Plant ◽  
Co2 Emission Reduction ◽  
Operational Strategy ◽  
Energy Demands

Some optimal operation methods based on the mixed-integer linear programming (MILP) have been proposed to operate energy supply plants properly from the viewpoints of economics, energy saving, and CO2 emission reduction. However, most of the methods are effective only under certain energy demands. In operating an energy supply plant actually, it is necessary to determine the operational strategy properly based on predicted energy demands. In this case, realized energy demands may differ from the predicted ones. Therefore, it is necessary to determine the operational strategy so that it is robust against the uncertainty in energy demands. In this paper, an optimization method based on the MILP is proposed to conduct the robust optimal operation of energy supply plants under uncertain energy demands. The uncertainty in energy demands is expressed by their intervals. The operational strategy is determined to minimize the maximum regret in the operational cost under the uncertainty. In addition, a hierarchical relationship among operation modes and on/off states of equipment, energy demands, and energy flow rates of equipment is taken into account. First, a general formulation of a robust optimal operation problem is presented, which is followed by a general solution procedure. Then, in a numerical study, the proposed method is applied to a gas turbine cogeneration plant for district energy supply. Through the study, some features of the robust optimal operation are clarified, and the validity and effectiveness of the proposed method are ascertained.

scholarly journals Planning High-Level Paths in Hostile, Dynamic, and Uncertain Environments

2020 ◽  
Vol 69 ◽  
pp. 297-342
Author(s):  
Jacopo Banfi ◽  
Vikram Shree ◽  
Mark Campbell
Keyword(s):  
Large Scale ◽  
A Priori ◽  
Exact Algorithm ◽  
Mixed Integer ◽  
Planning Problem ◽  
Uncertain Environments ◽  
Large Scale Problems ◽  
High Level ◽  
Hostile Environments ◽  
Path Planning Problem

This paper introduces and studies a graph-based variant of the path planning problem arising in hostile environments. We consider a setting where an agent (e.g. a robot) must reach a given destination while avoiding being intercepted by probabilistic entities which exist in the graph with a given probability and move according to a probabilistic motion pattern known a priori. Given a goal vertex and a deadline to reach it, the agent must compute the path to the goal that maximizes its chances of survival. We study the computational complexity of the problem, and present two algorithms for computing high quality solutions in the general case: an exact algorithm based on Mixed-Integer Nonlinear Programming, working well in instances of moderate size, and a pseudo-polynomial time heuristic algorithm allowing to solve large scale problems in reasonable time. We also consider the two limit cases where the agent can survive with probability 0 or 1, and provide specialized algorithms to detect these kinds of situations more efficiently.

Analysis of Optimal Unit Numbers and Sizes for Microturbine Cogeneration Systems

2004 ◽  
Author(s):  
Satoshi Gamou ◽  
Koichi Ito ◽  
Ryohei Yokoyama
Keyword(s):  
Energy Demand ◽  
Large Scale ◽  
Maximum Energy ◽  
Mixed Integer ◽  
Small Scale ◽  
Optimization Approach ◽  
Operational Strategies ◽  
Energy Demands ◽  
Exhaust Heat ◽  
Annual Total

The relationships between unit numbers and capacities to be installed for microturbine cogeneration systems are analyzed from an economic viewpoint. In analyzing, an optimization approach is adopted. Namely, unit numbers and capacities are determined together with maximum contract demands of utilities such as electricity and natural gas so as to minimize the annual total cost in consideration of annual operational strategies corresponding to seasonal and hourly energy demand requirements. This optimization problem is formulated as a large-scale mixed-integer linear programming one. The suboptimal solution of this problem is obtained efficiently by solving several small-scale subproblems. Through numerical studies carried out on systems installed in hotels by changing the electrical generating/exhaust heat recovery efficiencies, the initial capital cost of the microturbine cogeneration unit and maximum energy demands as parameters, the influence of the parameters on the optimal numbers and capacities of the microturbine cogeneration units is clarified.

scholarly journals Partitioning and solving large-scale tactical harvest scheduling problems for industrial plantation forests

2020 ◽  
Vol 50 (8) ◽  
pp. 811-818
Author(s):  
Pedro Bellavenutte ◽  
Woodam Chung ◽  
Luis Diaz-Balteiro
Keyword(s):  
Large Scale ◽  
Optimization Procedure ◽  
Mixed Integer ◽  
Forest Planning ◽  
Planning Problem ◽  
Optimization Approach ◽  
Scheduling Problems ◽  
Harvest Scheduling ◽  
Plantation Forests ◽  
Industrial Plantation

Spatially explicit, tactical forest planning is a necessary but challenging task in the management of plantation forests. It involves harvest scheduling and planning for road access and log transportation over time and space. This combinatorial problem can be formulated into the fixed-charge transportation problem (FCTP), in which the sum of fixed and variable costs is minimized while meeting harvest volume requirements and allowing necessary road maintenance and log hauling activities. The problem can be solved using general optimization methods such as mixed-integer linear programming (MILP), but the computational efficiency of the MILP-based approach quickly drops as the size and complexity of the problem increases. We developed a new optimization procedure that partitions the large planning problem into smaller subproblems. We applied a hybrid optimization approach using both MILP and heuristic rules to efficiently solve the large FCTP that otherwise may not be solvable using traditional methods. We applied our approach to an industrial plantation forest in Brazil. Our applications demonstrate the performance of the new optimization procedure and the benefit of solving large forest planning problems that integrate harvest scheduling with road access and transportation.

scholarly journals The Airlift Planning Problem

2019 ◽  
Vol 53 (3) ◽  
pp. 773-795
Author(s):  
Dimitris Bertsimas ◽  
Allison Chang ◽  
Velibor V. Mišić ◽  
Nishanth Mundru
Keyword(s):  
Large Scale ◽  
Programming Model ◽  
Simulated Data ◽  
Mixed Integer ◽  
Planning Problem ◽  
Weight Restrictions ◽  
Total Delay ◽  
Data Set ◽  
Scale Optimization ◽  
Airlift Planning

The U.S. Transportation Command (USTRANSCOM) is responsible for planning and executing the transportation of U.S. military personnel and cargo by air, land, and sea. The airlift planning problem faced by the air component of USTRANSCOM is to decide how requirements (passengers and cargo) will be assigned to the available aircraft fleet and the sequence of pickups and drop-offs that each aircraft will perform to ensure that the requirements are delivered with minimal delay and with maximum utilization of the available aircraft. This problem is of significant interest to USTRANSCOM because of the highly time-sensitive nature of the requirements that are typically designated for delivery by airlift, as well as the very high cost of airlift operations. At the same time, the airlift planning problem is extremely difficult to solve because of the combinatorial nature of the problem and the numerous constraints present in the problem (such as weight restrictions and crew rest requirements). In this paper, we propose an approach for solving the airlift planning problem faced by USTRANSCOM based on modern, large-scale optimization. Our approach relies on solving a large-scale mixed-integer programming model that disentangles the assignment decision (which aircraft will pickup and deliver which requirement) from the sequencing decision (in what order the aircraft will pickup and deliver its assigned requirements), using a combination of heuristics and column generation. Through computational experiments with both a simulated data set and a planning data set provided by USTRANSCOM, we show that our approach leads to high-quality solutions for realistic instances (e.g., 100 aircraft and 100 requirements) within operationally feasible time frames. Compared with a baseline approach that emulates current practice at USTRANSCOM, our approach leads to reductions in total delay and aircraft time of 8%–12% in simulated data instances and 16%–40% in USTRANSCOM’s planning instances.

UAV Path Planning in the Framework of MILP-Tropical Optimization

2017 ◽  
Author(s):  
Mohammadreza Radmanesh ◽  
Manish Kumar
Keyword(s):  
Path Planning ◽  
Numerical Study ◽  
Mixed Integer ◽  
Planning Problem ◽  
Fast Method ◽  
Tropical Mathematics ◽  
Tropical Optimization ◽  
Multiple Vehicles ◽  
And Performance ◽  
New Framework

This paper proposes a fast method for obtaining mathematically optimal trajectories for UAVs while avoiding collisions. A comparison of the proposed method with previously used Mixed Integer Linear Programming (MILP) to find the optimal collision-free path UAVs, aircraft, and spacecraft show the effectiveness and performance of this method. Here, the UAV path planning problem is formulated in the new framework named MILP-Tropical optimization that exploits tropical mathematics for obtaining solution and then casted in a novel branch-and-bound method. Various constraints including UAV dynamics are incorporated in the proposed Tropical framework and a solution methodology is presented. An extensive numerical study shows that the proposed method provides faster solution. The proposed technique can be extended to distributed control for multiple vehicles and multiple way-points.

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