Ageing and Efficiency Aware Battery Dispatch for Arbitrage Markets Using Mixed Integer Linear Programming

To achieve maximum profit by dispatching a battery storage system in an arbitrage operation, multiple factors must be considered. While revenue from the application is determined by the time variability of the electricity cost, the profit will be lowered by costs resulting from energy efficiency losses, as well as by battery degradation. In this paper, an optimal dispatch strategy is proposed for storage systems trading on energy arbitrage markets. The dispatch is based on a computationally-efficient implementation of a mixed-integer linear programming method, with a cost function that includes variable-energy conversion losses and a cycle-induced battery capacity fade. The parametrisation of these non-linear functions is backed by in-house laboratory tests. A detailed analysis of the proposed methods is given through case studies of different cost-inclusion scenarios, as well as battery investment-cost scenarios. An evaluation with a sample intraday market data set, collected throughout 2017 in Germany, offers a potential monthly revenue of up to 8762 EUR/MWh cap installed capacity, without accounting for the costs attributed to energy losses and battery degradation. While this is slightly above the revenue attainable in a reference application—namely, primary frequency regulation for the same sample month (7716 EUR/MWh cap installed capacity)—the situation changes if costs are considered: The optimisation reveals that losses in battery ageing and efficiency reduce the attainable profit by up to 36% for the most profitable arbitrage use case considered herein. The findings underline the significance of considering both ageing and efficiency in battery system dispatch optimisation.

Download Full-text

Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems

INFORMS Journal on Computing ◽

10.1287/ijoc.2020.0976 ◽

2020 ◽

Cited By ~ 1

Author(s):

Álinson S. Xavier ◽

Feng Qiu ◽

Shabbir Ahmed

Keyword(s):

Machine Learning ◽

Linear Programming ◽

Integer Linear Programming ◽

Mixed Integer Linear Programming ◽

Large Scale ◽

Unit Commitment ◽

Energy Sector ◽

Mixed Integer ◽

Data Set ◽

Computational Performance

Security-constrained unit commitment (SCUC) is a fundamental problem in power systems and electricity markets. In practical settings, SCUC is repeatedly solved via mixed-integer linear programming (MIP), sometimes multiple times per day, with only minor changes in input data. In this work, we propose a number of machine learning techniques to effectively extract information from previously solved instances in order to significantly improve the computational performance of MIP solvers when solving similar instances in the future. Based on statistical data, we predict redundant constraints in the formulation, good initial feasible solutions, and affine subspaces where the optimal solution is likely to lie, leading to a significant reduction in problem size. Computational results on a diverse set of realistic and large-scale instances show that using the proposed techniques, SCUC can be solved on average 4.3 times faster with optimality guarantees and 10.2 times faster without optimality guarantees, with no observed reduction in solution quality. Out-of-distribution experiments provide evidence that the method is somewhat robust against data-set shift. Summary of Contribution. The paper describes a novel computational method, based on a combination of mixed-integer linear programming (MILP) and machine learning (ML), to solve a challenging and fundamental optimization problem in the energy sector. The method advances the state-of-the-art, not only for this particular problem, but also, more generally, in solving discrete optimization problems via ML. We expect that the techniques presented can be readily used by practitioners in the energy sector and adapted, by researchers in other fields, to other challenging operations research problems that are solved routinely.

Download Full-text

Analysis of network design for a circular production system using multi-objective mixed integer linear programming model

Journal of Manufacturing Technology Management ◽

10.1108/jmtm-02-2018-0058 ◽

2019 ◽

Vol 30 (3) ◽

pp. 628-646 ◽

Cited By ~ 8

Author(s):

K.E.K. Vimal ◽

Sonu Rajak ◽

Jayakrishna Kandasamy

Keyword(s):

Mathematical Model ◽

Linear Programming ◽

Integer Linear Programming ◽

Mixed Integer Linear Programming ◽

Economic Benefits ◽

Mixed Integer ◽

Data Set ◽

Content Type ◽

Multi Objective ◽

Case Organization

PurposeThe purpose of this paper is to propose a mathematical model for the design of a circular production system (CPS) for an Indian manufacturing organization participating in a symbiotic network.Design/methodology/approachA multi-objective mixed integer linear programming is used to model the network for quantifying the economic benefits. The data set collected from the case organization is used. The GAMS optimization package is used to simulate the model.FindingsThe model is able to compute the economic benefit achieved through circular operations in the case organization. The flow of different items through the network is also obtained.Research limitations/implicationsThe data set of the single organization in the symbiotic network is used to validate the proposed mathematical model. Further research can be done considering the all the organizations in the considered symbiotic network.Practical implicationsThis paper will help the authors to better understand the role of sustainable supply chains in a circular economy model especially in energy and materials intensive industries.Originality/valueThis study has uniquely utilized a multi-objective mixed-integer linear programming approach for the analysis of variables in CPSs and the corresponding economic benefits.

Download Full-text

A mixed-integer linear programming model for the selective full-truckload multi-depot vehicle routing problem with time windows

Decision Science Letters ◽

10.5267/j.dsl.2021.7.002 ◽

2021 ◽

Vol 10 (4) ◽

pp. 471-486 ◽

Cited By ~ 1

Author(s):

Karim EL Bouyahyiouy ◽

Adil Bellabdaoui

Keyword(s):

Linear Programming ◽

Vehicle Routing ◽

Integer Linear Programming ◽

Vehicle Routing Problem ◽

Mixed Integer Linear Programming ◽

Programming Model ◽

Time Windows ◽

Mixed Integer ◽

Data Set ◽

Routing Problem

This article has studied a full truckload transportation problem in the context of an empty return scenario, particularly an order selection and vehicle routing problem with full truckload, multiple depots and time windows (SFTMDVRPTW). The aim is to develop a solution where a set of truck routes serves a subset of selected transportation demands from a number of full truckload orders to maximize the total profit obtained from those orders. Each truck route is a chain of selected demands to serve, originating at a departure point and terminating at an arriving point of trucks in a way that respects the constraints of availability and time windows. It is not mandatory to serve all orders, and only the profitable ones are selected. In this study, we have formulated the SFTMDVRPTW as a mixed-integer linear programming (MILP) model. Finally, Computational results are conducted on a new data set that contains thirty randomly generated problem instances ranging from 16 to 30 orders using the CPLEX software. The findings prove that our model has provided good solutions in a reasonable time.

Download Full-text