Job scheduling under Time-of-Use energy tariffs for sustainable manufacturing: a survey

Catanzaro, Daniele; Pesenti, Raffaele; Ronco, Roberto

doi:10.1016/j.ejor.2023.01.029

Cited by 10 publications

(2 citation statements)

References 92 publications

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“…Economists almost unanimously agree that time-of-use pricing (also known as peak pricing and peak–valley pricing) can improve the efficiency of the power system, reduce the cost of electricity (Kiguchi et al ., 2021), increase investment in distributed photovoltaic projects (Darghouth et al ., 2011) and reduce carbon emission levels (Holland and Mansur, 2008). The implementation of time-of-use pricing is intended to encourage electricity use at low prices during off-peak hours, thereby suppressing peak electricity demand (Catanzaro et al ., 2023). Some quantitative studies have found that under time-of-use pricing, customers will shift their power demand from peak to trough periods to reduce their electricity costs (Faruqui and Sergici, 2010), which tends to flatten peak demand (Hu et al ., 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

Capacity investment decisions of energy storage power stations supporting wind power projects

Song,

Kong,

Xie

2023

IMDS

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PurposeRapidly increasing the proportion of installed wind power capacity with zero carbon emission characteristics will help adjust the energy structure and support the realization of carbon neutrality targets. The intermittency of wind resources and fluctuations in electricity demand has exacerbated the contradiction between power supply and demand. The time-of-use pricing and supply-side allocation of energy storage power stations will help “peak shaving and valley filling” and reduce the gap between power supply and demand. To this end, this paper constructs a decision-making model for the capacity investment of energy storage power stations under time-of-use pricing, which is intended to provide a reference for scientific decision-making on electricity prices and energy storage power station capacity.Design/methodology/approachBased on the research framework of time-of-use pricing, this paper constructs a profit-maximizing electricity price and capacity investment decision model of energy storage power station for flat pricing and time-of-use pricing respectively. In the process, this study considers the dual uncertain scenarios of intermittency of wind resources and random fluctuations in power demand.Findings(1) Investment in energy storage power stations is the optimal decision. Time-of-use pricing will reduce the optimal capacity of the energy storage power station. (2) The optimal capacity of the energy storage power station and optimal electricity price are related to factors such as the intermittency of wind resources, the unit investment cost, the price sensitivities of the demand, the proportion of time-of-use pricing and the thermal power price. (3) The carbon emission level is affected by the intermittency of wind resources, price sensitivities of the demand and the proportion of time-of-use pricing. Incentive policies can always reduce carbon emission levels.Originality/valueThis paper creatively introduced the research framework of time-of-use pricing into the capacity decision-making of energy storage power stations, and considering the influence of wind power intermittentness and power demand fluctuations, constructed the capacity investment decision model of energy storage power stations under different pricing methods, and compared the impact of pricing methods on optimal energy storage power station capacity and carbon emissions.HighlightsElectricity pricing and capacity of energy storage power stations in an uncertain electricity market.Investment strategy of energy storage power stations on the supply side of wind power generators.Impact of pricing method on the investment decisions of energy storage power stations.Impact of pricing method, energy storage investment and incentive policies on carbon emissions.A two-stage wind power supply chain including energy storage power stations.

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Section: Literature Reviewmentioning

confidence: 99%

Capacity investment decisions of energy storage power stations supporting wind power projects

Song,

Kong,

Xie

2023

IMDS

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“…Among the energy-related performance measures, energy consumption is the most used one. More recently and in response to the utility's current trend of introducing time-dependent energy prices, such as time of use tariffs and critical peak pricing, many works minimizing energy costs have been reported (see, e.g., Borges et al, 2020;Catanzaro et al, 2023). often, it is not possible or prohibitively expensive to go beyond the power limit contracted.…”

Section: Introductionmentioning

confidence: 99%

Optimizing job shop scheduling with speed‐adjustable machines and peak power constraints: A mathematical model and heuristic solutions

Homayouni,

Fontes

2023

Int Trans Operational Res

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This paper addresses a job shop scheduling problem with peak power constraints, in which jobs can be processed once or multiple times on either all or a subset of the machines. The latter characteristic provides additional flexibility, nowadays present in many manufacturing systems. The problem is complicated by the need to determine both the operation sequence and starting time as well as the speed at which machines process each operation. Due to the adherence to renewable energy production and its intermittent nature, manufacturing companies need to adopt power‐flexible production schedules. The proposed power control strategies, that is, adjusting processing speed and timing to reduce peak power requirements may impact production time (makespan) and energy consumption. Therefore, we propose a bi‐objective approach that minimizes both objectives. A linear programming model is developed to provide a formal statement of the problem, which is solved to optimality for small‐sized instances. We also proposed a multi‐objective biased random key genetic algorithm framework that evolves several populations in parallel. Computational experiments provide decision and policymakers with insights into the implications of imposing or negotiating power consumption limits. Finally, the several trade‐off solutions obtained show that as the power limit is lowered, the makespan increases at an increasing rate and a similar trend is observed in energy consumption but only for very small makespan values. Furthermore, peak power demand reductions of about 25% have a limited impact on the minimum makespan value (4–6% increase), while at the same time allowing for a small reduction in energy consumption.

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A Computational Journey in Job Scheduling with Time-of-Use Costs

Gaggero,

Paolucci,

Ronco

2024

AIRO Springer Series

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Job scheduling under Time-of-Use energy tariffs for sustainable manufacturing: a survey

Cited by 10 publications

References 92 publications

Capacity investment decisions of energy storage power stations supporting wind power projects

Capacity investment decisions of energy storage power stations supporting wind power projects

Optimizing job shop scheduling with speed‐adjustable machines and peak power constraints: A mathematical model and heuristic solutions

A Computational Journey in Job Scheduling with Time-of-Use Costs

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