Time Of Use Electricity Tariffs Research Articles

A two-step scheduling and rescheduling framework for integrated production and usage-based maintenance planning under TOU electricity tariffs: A case study of the tile industry

The primary motivation of this study is to provide an easily implementable and cost-effective solution for reducing energy expenses, improving slurry quality, mitigating system instability due to random breakdowns, and minimizing maintenance frequencies in the ball mills of the tile industry. To achieve this goal, a two-step framework is proposed which can also be applied to parallel machines in make-to-order environments. The first step integrates the usage-based maintenance and production scheduling under time-of-use (TOU) electricity prices and involves a mixed integer linear programming (MILP) model with buffer time insertion to handle unpredictable machine unavailability. The second step suggests a combination of model-based and manual reactive policies for rescheduling. Compared to its non-energy-aware counterpart, the proposed model shows a 22% improvement in energy costs and a 4% increase in energy savings. Additionally, the usage-based maintenance strategy reduces maintenance frequencies by more than 50% compared to the time-based one. By integrating TOU-based production scheduling with usage-based maintenance scheduling, we offer three advantages for energy-intensive industries: 1) increased scheduling flexibility to shift production to low-tariff periods, 2) the ability to schedule maintenance operations during idle time resulting from production shifts, and 3) reduced maintenance frequencies for machines with extended idle times through a usage-based maintenance strategy. The proposed approach performs best when machines exhibit significant differences in energy consumption rates and have high total idle times. However, implementing it for bottleneck machines does not lead to notable improvements in energy-related indicators.

Economic analysis of integrating photovoltaics and battery energy storage system in an office building

The concept of ‘Active Building’ refers to any building, such as factories, offices, homes, and other structures in the built environment, which are equipped to conserve, generate, store, and release energy in the UK. One such Active Building was built at Swansea University in the UK in 2018 and is currently used as a small office including well fare facilities. The objective of this study is to analyse the economic performance of an Active Building, incorporating building-integrated photovoltaics (BIPV) and lithium-ion (Li-ion) batteries with real building operational profiles and metered energy load profiles. The cost covers the capital cost of 22 kWp BIPV and 110 kWh Li-ion battery, and electricity cost from the electric grid with two types of time of use electricity tariffs - South Wales (SW) time of use tariff and Red, Amber, and Green (RAG) rates, and the potential carbon cost of electricity supplied from the Wales electric grid and generated from the Active Building. Four battery operational strategies are designed, and battery status of charge and electricity flow are monitored. The analysis is undertaken on the BIPV units with or without Li-ion batteries under various scenarios. The results show that the investment of BIPV units without Li-ion batteries can make a profit within the lifetime of BIPV in the current electricity market. However, the current Li-ion battery storage does not compensate for its capital cost from the reduced economic value obtained from the BIPV system when the electricity is curtailed. Even though the current economic analysis of the Active Building installing BIPV and battery is not convincing at the current capital price, this combination can enable the Active Building to be independent of the electric grid and to balance electricity demand and generation itself. The analysis will demonstrate the market conditions required to make these operational benefits cost-effective.

Energy-efficient single-machine scheduling with release dates under time-of-use electricity tariffs

The global trend of increasing resource utilization in industry has drawn great attention from researchers and practitioners. Industrial managers are increasingly optimizing their production strategies based on time-of-use (TOU) electricity tariffs to reduce energy consumption costs. This study investigates a new energy-efficient single-machine scheduling problem with release dates under TOU electricity tariffs. It consists in sequencing a set of jobs with release dates to a machine under TOU pricing to minimize the total energy cost given a bounded maximum completion time. We first formulate the problem using the widely used time-indexed mixed-integer linear programming (T-MILP). Due to its time-consuming disadvantage, a new period-based MILP (P-MILP) model is developed based on the characteristics of the problem. Given the NP-hardness of the problem, a two-stage heuristic (TSH) algorithm is proposed to solve practical-sized problems. Specifically, in the first stage, a constructive heuristic is designed to obtain an initial solution, and in the second one, a tailored tabu search is devised to yield a better satisfactory solution. Numerical experiments are conducted on a real-life case and 1150 randomly generated instances with up to 1000 jobs. Computational results show that (i) the proposed models can save the total electricity cost by about 30% compared with an existing empirical scheduling method when solving small-sized instances; (ii) the efficiency of P-MILP is about 140.94% higher than that of T-MILP; and (iii) the proposed TSH algorithm can efficiently obtain high-quality solutions for practical-sized instances with gaps of less than 7% compared to the lower bounds, which can effectively support production managers to save the energy costs in practical production.

Hourly occupant clothing decisions in residential HVAC energy management

The state-of-the-art energy management for the heating, ventilation and air conditioning (HVAC) system uses a static clothing model that calculates the occupant's clothing insulation as a fixed value based on outdoor air temperature measured at a particular time of the day. However, the static clothing model can hardly capture the occupant's intra-day clothing behaviors, leading to inaccurate thermal comfort assessment and unrealistic HVAC energy management. This paper proposes a novel HVAC energy management scheme to optimally schedule the thermostat setpoints of HVAC and to provide recommendations on occupants' optimal hourly clothing decisions through a predicted mean vote model, while considering uncertainties in the outside temperature. The proposed HVAC energy management scheme is solved by applying an approximate dynamic programming approach. Further, a model predictive control framework with a long short-term memory based forecaster is developed for more realistic simulations. We study the HVAC schedules in a residential home with summer and winter time of use electricity tariffs for both male and female occupants. Compared with non-optimized cases, proof-of-concept simulation results demonstrate that the proposed scheme can achieve a 53.8% and a 29.8% cost saving in a summer-male scenario and a winter-female scenario, respectively.

Energy Saving for Tissue Paper Mills by Energy-Efficiency Scheduling under Time-of-Use Electricity Tariffs

Environmental concerns and soaring energy prices have brought huge pressure of energy saving and emission reduction to tissue paper mills. Electricity is one of the main energy sources of tissue paper mills. The production characteristics of tissue paper mills make it easy to decrease energy cost by using time-of-use (TOU) electricity tariffs. This study investigates the bi-objective energy-efficiency scheduling of tissue paper mills under time-of-use electricity tariffs, the objectives of which are makespan and energy cost. First, considering the processing energy cost, setup energy cost, and transportation energy cost, an energy cost model of a tissue paper mill under TOU electricity tariffs is established. Second, the energy-efficiency scheduling model under TOU electricity tariffs is built based on the energy cost model. Finally, on the basis of decomposition and teaching–learning optimization, this study proposes a novel multi-objective evolutionary algorithm and further combined with the variable neighborhood search to solve the problem. The case study results demonstrate that our study of tissue paper mill energy saving is feasible, and the proposed method has better performance than the existing methods.

The potential of energy flexibility of space heating and cooling in Portugal

This study assesses the potential of energy flexibility of space heating and cooling for a typical household under different geographical conditions in Portugal. The proposed approach modifies the demand through the optimization of the thermostat settings using a genetic algorithm to reduce either operational costs or interaction with the grid. The results show that the used energy flexibility indicator expresses the available potential and that flexibility depends on several factors, namely: i) thermal inertia of the archetypical household; ii) the time of use electricity tariffs; iii) users’ comfort boundaries; and iv) the geographical location of the houses.

Bi-Objective Re-Entrant Hybrid Flow Shop Scheduling considering Energy Consumption Cost under Time-of-Use Electricity Tariffs

Re-entrant hybrid flow shop scheduling problem (RHFSP) is widely used in industries. However, little attention is paid to energy consumption cost with the raise of green manufacturing concept. This paper proposes an improved multiobjective ant lion optimization (IMOALO) algorithm to solve the RHFSP with the objectives of minimizing the makespan and energy consumption cost under Time-of-Use (TOU) electricity tariffs. A right-shift operation is then used to adjust the starting time of operations by avoiding the period of high electricity price to reduce the energy consumption cost as far as possible. The experimental results show that IMOALO algorithm is superior to multiobjective ant lion optimization (MOALO) algorithm, NSGA-II, and MOPSO in terms of the convergence, dominance, and diversity of nondominated solutions. The proposed model can make enterprises avoid high price period reasonably, transfer power load, and reduce the energy consumption cost effectively. Meanwhile, parameter analysis indicates that the period of TOU electricity tariffs and energy efficiency of machines have great impact on the scheduling results.

Large-scale energy-conscious bi-objective single-machine batch scheduling under time-of-use electricity tariffs via effective iterative heuristics

Time-of-use (TOU) electricity pricing policy is widely encountered in the world, which provides new opportunities for power-intensive enterprises to save their energy cost. A good trade-off between the total electricity cost and production efficiency is desired by decision makers. This work addresses an energy-conscious bi-objective single-machine batch scheduling problem under TOU electricity tariffs, in which electricity price varies with time. The objective of the problem is to simultaneously minimize total electricity cost and makespan. Due to its strong NP-hard nature, two fast new ϵ-constraint-based constructive heuristic algorithms are developed to solve it. The core idea is to transform the bi-objective problem into a series of single-objective problems that are fast and heuristically solved to obtain an approximate Pareto front. Especially, for each transformed single-objective problem, two novel constructive heuristic algorithms are proposed by solving a series of multiple knapsack problems and 0–1 knapsack problems, respectively. Computational results on 145 benchmark and 80 newly generated larger-scale instances show that the proposed algorithms are quite efficient and are able to find high-quality Pareto solutions for large-scale problems with up to 200 batches.

Optimization for energy-efficient flexible flow shop scheduling under time of use electricity tariffs

With the development of society and economy, the global energy problem has become increasingly critical. Methods for time of use electricity tariffs have been introduced to balance the demand for resource and increase the energy efficiency in manufacturing industry. The scheduling problem for flexible flow shop in the environment of TOU is studied in this paper. An energy consumption model of machine tools is established which involves the processing energy, standby energy and set-up energy for a detailed description of electricity cost in shop-floor. The Improved Strength Pareto Evolutionary Algorithm is employed in which a novel gene encoding considering processing sequence, machine tool and processing time simultaneously is proposed to obtain the Pareto Front of the makespan and electricity cost. A case study is presented and the result shows that the proposed method is applicable for trade-off of electricity cost and delivery time for the purpose of enhanced energy efficiency.

A New Greedy Insertion Heuristic Algorithm with a Multi-Stage Filtering Mechanism for Energy-Efficient Single Machine Scheduling Problems

To improve energy efficiency and maintain the stability of the power grid, time-of-use (TOU) electricity tariffs have been widely used around the world, which bring both opportunities and challenges to the energy-efficient scheduling problems. Single machine scheduling problems under TOU electricity tariffs are of great significance both in theory and practice. Although methods based on discrete-time or continuous-time models have been put forward for addressing this problem, they are deficient in solution quality or time complexity, especially when dealing with large-size instances. To address large-scale problems more efficiently, a new greedy insertion heuristic algorithm with a multi-stage filtering mechanism including coarse granularity and fine granularity filtering is developed in this paper. Based on the concentration and diffusion strategy, the algorithm can quickly filter out many impossible positions in the coarse granularity filtering stage, and then, each job can find its optimal position in a relatively large space in the fine granularity filtering stage. To show the effectiveness and computational process of the proposed algorithm, a real case study is provided. Furthermore, two sets of contrast experiments are conducted, aiming to demonstrate the good application of the algorithm. The experiments indicate that the small-size instances can be solved within 0.02 s using our algorithm, and the accuracy is further improved. For the large-size instances, the computation speed of our algorithm is improved greatly compared with the classic greedy insertion heuristic algorithm.

Techno-economic and environmental optimization of a household photovoltaic-battery hybrid power system within demand side management

This paper presents a power management system of a household photovoltaic-battery hybrid power system within demand side management under time of use electricity tariff. This system is easy to implement by employing cheap electrical switches, off-the-shelf chargers and inverters. Control system models combining both power dispatching level and home appliance scheduling level are proposed to minimize the residents' energy cost and energy consumption from the grid with the practical constraints strictly satisfied. In addition, the resident comfort inconvenience level is considered in the control system models. The trade-off among operating cost, energy consumption and inconvenience is considered and a multi-objective optimization problem is formulated. The optimal control strategies are derived by solving a mixed-integer nonlinear programming problem. Simulation results show that the energy cost and energy consumption from the grid can be largely reduced with the proposed strategies. These results are important for customers to dispel their major uncertainty in determining whether to newly install or update to such photovoltaic-battery hybrid power systems.

Optimal scheduling of household appliances with a battery storage system and coordination

This paper demonstrates an optimal household appliance scheduling problem with a battery as an energy storage system under time of use electricity tariff. Power consumption measurements of individual appliances considered were performed and demand profiles were obtained. In this work, a mixed integer nonlinear programming mathematical model with more practical operation constraints for appliance and battery scheduling is formulated and solved. The simulation results show effectiveness of the algorithm in that by optimally scheduling appliances and battery, cost saving, peak shaving and valley filling are achieved through load shifting. The energy cost saving that might be beneficial to consumers; and peak shaving and valley filling, which are of great importance to the utility. It is found that consideration of appliance coordination yields smaller cost saving because of interdependent operation. Without the battery and coordination, a cost saving of 22% and peak reduction from 10.355kW to 8.405kW are realized. Consideration of appliance coordination gives a further cost saving of 1% and a relatively smaller peak reduction to 8.30kW. The battery bank system promotes peak shaving and valley filling and a further cost saving of about 6% and peak reduction to 5.175kW. Sensitivity analysis, however, reveals that the energy cost saving is sensitive to consumer's willingness to pay.