Flow Shop Scheduling for Energy Efficient Manufacturing

A large number of new peaking power plants with their associated auxiliary equipment are installed to meet the growing peak demand every year. However, 10% utility capacity is used for only 1%~2% of the hours in a year. Thus, to meet the demand and supply balance through increasing the infrastructure investments only on the supply side is not economical. Alternatively, demand-side management might cut the cost of maintaining this balance via offering consumers incentives to manage their consumption in response to the price signals. Time-varying electricity rate is a demand-side management scheme. Under the time-varying electricity rate, the electricity price is high during the peak demand periods, while it is low during the off-peak times. Thus, consumers might get the cost benefits through shifting power usages from the high price periods to the low price periods, which leading to reduce the peak power of the grid. The current research works on the price-based demand-side management are primarily focusing on residential and commercial users through optimizing the “shiftable” appliance schedules. A few research works have been done focusing manufacturing facilities. However, residential, commercial and industrial sectors each occupies about one-third of the total electricity consumption. Thus, this thesis investigates the flow shop scheduling problems that reduce electricity costs under time-varying electricity rate. A time-indexed integer programming is proposed to identify the manufacturing schedules that minimize the electricity cost for a single factory with flow shops under time-of-use (TOU) rate. The result shows that a 6.9% of electricity cost reduction can be reached by shifting power usage from on-peak period to other periods. However, in the case when a group of factories served by one utility, each factory shifting power usage from on-peak period to off-peak hours independently, which might change the time of peak demand periods. Thus, a TOU pricing combined with inclining block rate (IBR) is proposed to avoid this issue. Two optimization problems are studied to demonstrate this approach. Each factory optimizes manufacturing schedule to minimize its electricity cost: (1) under TOU pricing, and (2) under TOU-IBR pricing. The results show that the electricity cost of each factory is minimized, but the total electricity cost at the 2nd hour is 6.25% beyond the threshold under TOU pricing. It also shows that factories collaborate with each other to minimize the electricity cost, and meanwhile, the power demand at each hour is not larger than the thresholds under TOU-IBR pricing. In contrast to TOU rate, the electricity price cannot be determined in ahead under real-time price (RTP), since it is dependent on the total energy consumption of the grid. Thus, the interactions between electricity market and the manufacturing schedules bring additional challenges. To address this issue, the time-indexed integer programming is developed to identify the manufacturing schedule that has the minimal electricity cost of a factory under the RTP. This approach is demonstrated using a manufacturing facility with flow shops operating during different time periods in a microgrid which also served residential and commercial buildings. The results show that electricity cost reduction can be achieved by 6.3%, 10.8%, and 24.8% for these three time periods, respectively. The total cost saving of manufacturing facility is 15.1% over this 24-hour period. The results also show that although residential and commercial users are under “business-as-usual” situation, their electricity costs can also be changed due to the power demand changing in the manufacturing facilities. Furthermore, multi-manufacturing factories served by one utility are investigated. The manufacturing schedules of a group of manufacturing facilities with flow shops subject to the RTP are optimized to minimize their electricity cost. This problem can be formulated as a centralized optimization problem. Alternatively, this optimization problem can be decomposed into several pieces. A heuristic approach is proposed to optimize the sub-optimization problems in parallel. The result shows that both the individual and total electricity cost of factories are minimized and meanwhile the computation time is reduced compared with the centralized algorithm

Intelligent Optimization Systems for MaintenanceScheduling of Power Plant Generators

This paper presents a Genetic Algorithm (GA) and Ant-Colony (AC) optimization model for power plant generators’ maintenance scheduling. Maintenance scheduling of power plant generators is essential for ensuring the reliability and economic operation of a power system. Proper maintenance scheduling prolongs the shelf life of the generators and prevents unexpected failures. To reduce the cost and duration of generator maintenance, these models are built with various constants, fitness functions, and objective functions. The Analytical Hierarchy Process (AHP), a decision-making tool, is implemented to aid the researcher in prioritizing and re-ranking the maintenance activities from the most important to the least. The intelligent optimization models are developed using MATLAB and the developed intelligent algorithms are tested on a case study in a coal power plant located at minjung, Perak, Malaysia. The power plant is owned and operated by Tenaga Nasional Berhad (TNB), the electric utility company in peninsular Malaysia. The results show that GA outperforms ACO since it reduces maintenance costs by 39.78% and maintenance duration by 60%. The study demonstrates that the proposed optimization method is effective in reducing maintenance time and cost while also optimizing power plant operation

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Residential Demand Side Management model, optimization and future perspective: A review

The residential load sector plays a vital role in terms of its impact on overall power balance, stability, and efficient power management. However, the load dynamics of the energy demand of residential users are always nonlinear, uncontrollable, and inelastic concerning power grid regulation and management. The integration of distributed generations (DGs) and advancement of information and communication technology (ICT) even though handles the related issues and challenges up to some extent, till the flexibility, energy management and scheduling with better planning are necessary for the residential sector to achieve better grid stability and efficiency. To address these issues, it is indispensable to analyze the demand-side management (DSM) for the complex residential sector considering various operational constraints, objectives, identifying various factors that affect better planning, scheduling, and management, to project the key features of various approaches and possible future research directions. This review has been done based on the related literature to focus on modeling, optimization methods, major objectives, system operation constraints, dominating factors impacting overall system operation, and possible solutions enhancing residential DSM operation. Gaps in future research and possible prospects have been discussed briefly to give a proper insight into the current implementation of DSM. This extensive review of residential DSM will help all the researchers in this area to innovate better energy management strategies and reduce the effect of system uncertainties, variations, and constraints

Operation of The Hybrid Energy Resources with Storage System Participation

This paper focused on the optimal operation of the hybrid energy resources in the off-grid state considering energy storage participation. The hybrid energy resources consist of wind turbine (WT), photovoltaic (PV), diesel generator (DG), and energy storage system for supplying energy to DC and AC load demand with maximum reliability. The operation of the proposed energy system based on energy control and energy optimization is modeled. The energy optimization and energy control are implemented by heuristic and nonlinear quadratic programming approaches via optimal power flow on the resources side. The energy control is done based on the weight sum method in different operation states of the system. Also, the impact of the energy storage system on the hybrid energy resources is considered as backup resources. The energy control modeling is implemented via mathematical simulation and numerical analysis in the two operation states in the summer and winter seasons for verifying the proposed approaches. Finally, the results of the energy control show optimal states of the energy system in supplying demand with considering the energy storage system

Operation of The Hybrid Energy Resources with Storage System Participation

This paper focused on the optimal operation of the hybrid energy resources in the off-grid state considering energy storage participation. The hybrid energy resources consist of wind turbine (WT), photovoltaic (PV), diesel generator (DG), and energy storage system for supplying energy to DC and AC load demand with maximum reliability. The operation of the proposed energy system based on energy control and energy optimization is modeled. The energy optimization and energy control are implemented by heuristic and nonlinear quadratic programming approaches via optimal power flow on the resources side. The energy control is done based on the weight sum method in different operation states of the system. Also, the impact of the energy storage system on the hybrid energy resources is considered as backup resources. The energy control modeling is implemented via mathematical simulation and numerical analysis in the two operation states in the summer and winter seasons for verifying the proposed approaches. Finally, the results of the energy control show optimal states of the energy system in supplying demand with considering the energy storage system

Utilization of Electric Prosumer Flexibility Incentivized by Spot and Balancing Markets

The use of energy flexibility to balance electricity demand and supply is becoming increasingly important due to the growing share of fluctuating energy sources. Electric flexibility regarding time or magnitude of consumption can be offered in the form of different products on electricity spot and balancing power markets. In the wake of the energy transition and because of new possibilities provided by digitalization, the decision intervals on these markets are becoming shorter and the controllability of electricity consumption and generation more small-scale. This evolution opens up new chances for formerly passive energy consumers. This thesis shows how electric flexibility can be monetized using the application example of commercial sites. These are often multimodal energy systems coupling electricity, heat, and gas, and thus deliver high flexibility potential. To leverage this potential, a comprehensive picture of demand-side flexibilization is provided and used to propose an energy management system and optimization for cost-optimized device schedules. The cost-optimization considers two simultaneous incentives: variable day-ahead spot market prices and revenues for offering possible schedule adjustments to the automatic Frequency Restoration Reserve (aFRR) balancing market. To solve the formulated optimization problem, a genetic algorithm is presented, tailored to the specific needs of consumers. In addition to addressing the trade-off between the two competing markets, the algorithm inherently considers the uncertain activation of aFRR bids and related catch-up effects. An analysis of the activation behavior of aFRR balancing market bids, based on a developed ex-post simulation, forms an important decision basis for the optimization. Finally, a simulation study concentrating on battery energy storage systems and combined heat and power plants on the consumer side enables the quantitative discussion of the optimization potential. The results show that consumers considering both markets simultaneously can achieve cost benefits that are up to multiples of those for pure day-ahead price optimization, despite the stochastic nature of aFRR balancing power activations. In conclusion, this thesis enables formerly passive electricity consumers to assume the role of alternative balancing service providers, hence contributing to the economic and reliable operation of power grids characterized by a high share of renewable energy sources