Energy Management using Industrial Flexibility with Multi-objective Distributed Optimization

In this paper, the multi-objective optimal power flow (MOOPF) problem optimization objectives focus on four optimization objectives: generation cost, emission, real power loss and voltage deviation (VD). Three renewable energy sources with successful industrial applications, including wind energy, solar energy, and tidal energy are introduced. Renewable energy supply is uncertain, so Weibull distribution probability, lognormal probability and Gumbel probability are used to calculate the instability and intermittency of wind energy, solar energy and tidal energy, respectively. The inclusion of four energy supplies on the IEEE-30 test system and the consideration of renewable energy reserves and penalty cost calculation improve the realism of the model. In order to obtain the control parameters that minimize the four optimization objectives, a named multi-objective pathfinder algorithm (MOPFA) based on elite dominance and crowding distance was proposed to solve this multi-objective optimization problem. Simulation results show the feasibility of the model, and MOPFA can get more evenly distributed Pareto front and provide more diverse solutions. A compromise solution was selected by the fuzzy decision system. Comparison with the recently published literature also shows that the proposed model can effectively reduce emissions and other indicators. In addition, the statistical test results show that MOPFA's multi-objective optimization performance ranks first. In solving this complex optimization problem, results show the MOPFA is superior to other multi-objective algorithms in optimization accuracy and speed.

A multi-objective particle swarm optimization (MOPSO) approach for multi-objective economic load dispatch problem in power system is presented in this paper. The economic load dispatch problem is a non-linear constrained multi-objective optimization problem. The proposed MOPSO approach handles the problem as a multi-objective problem with competing and non-commensurable fuel cost, emission and system loss objectives and has a diversity-preserving mechanism using an external memory (call “repository”) and a geographically-based approach to find widely different Pareto-optimal solutions. In addition, fuzzy set theory is employed to extract the best compromise solution. Several optimization runs of the proposed MOPSO approach were carried out on the standard IEEE 30-bus test system. The results revealed the capabilities of the proposed MOPSO approach to generate well-distributed Pareto-optimal non-dominated solutions of multi-objective economic load dispatch. Comparison with Multi-objective Evolutionary Algorithm (MOEA) showed the superiority of the proposed MOPSO approach and confirmed its potential for solving multi-objective economic load dispatch.

In this article, an efficient optimization procedure based on herding behavior of krill individuals, krill herd algorithm (KHA), for the solution of multi-objective optimal power flow (OPF) with the objective of fuel cost minimization, voltage deviation minimization and voltage stability improvement is proposed. This algorithm is based on the effect of the influence of a teacher on the output of learners in a class. The proposed KHA approach is carried out on the standard IEEE 30-bus, IEEE 57-bus and IEEE 118-bus systems to solve different single and multi-objective OPF problems. Simulation results of the proposed approach are compared to differential evolution (DE), modified DE (MDE), multi-objective DE (MODE), evolving ant direction DE (EADDE), particle swarm optimization (PSO), improved genetic algorithm (IGA), gradient method, biogeography-based optimization (BBO), PSO with inertia weight approach (PSOIWA), PSO with constriction factor approach (PSOCFA), real-coded GA (RGA), artificial bee colony (ABC), general passive congregation PSO (GPAC), local passive congregation PSO (LPAC), coordinated aggregation (CA), interior point method (IPM) and quantum-inspired evolutionary algorithm (QEA). The comparison demonstrates the superiority of the proposed approach and confirms its potential to solve multi-objective OPF problems. Copyright © 2014 John Wiley & Sons, Ltd.

Although various optimization methods for solving single-objective problems have been developed in the last few decades, these methods have lost their eligibility due to the fact that today’s engineering problems are toward multiple objective optimization problems, in real applications. For single-objective optimization problems, for example, in case of a minimization problem, this value is the decision vector giving the smallest objective that can be achieved within the specified constraints. Hence the minimum decision vector within all possible (feasible) solution vectors is the so-called optimal solution and/or optimal design. However, in multi-objective optimization problems, since a different objective value is generated against each decision vector, the superiority of the solutions over each other is determined by considering the trade-off among the objective values. Therefore, the solution of multi-objective optimization problems, unlike single-objective problems, is a set of vectors rather than a single decision vector. In multi-objective optimization problems, especially if there are intricate objectives, the computational cost of the problem increases. In other words, while synchronously trying to maximize one of the objectives and to minimize another one makes it difficult to find the global optimum design. One of the important techniques used in multi-objective optimization problems is Pareto optimality which enables to select the global optimum solution taking into account the trade-off among all objectives. In this context, using of derivative-based methods has decreased, but the use of metaheuristic methods has increased due to the rapid availability of global optimum solution. This is because the improvements in the field of optimization are progressing in proportion to technology and varying according to the needs. In this chapter, one of the recent metaheuristic optimization methods based on swarm intelligence that is so-called a Pareto-based bat algorithm inspired by the behavior of determining the direction and distance of an object using the echo of the sound called the echolocation of bats is used in order to obtain optimum solutions for multi-objective engineering design problems. In this regard, a four-bar planar truss, a real-sized welded steel beam as well as a multi-layer radar absorber are selected as multi-objective engineering design optimization problems. In case the obtained results (optimal designs) are examined, the potency and the reliability of the proposed multi-objective Pareto-based bat algorithm are demonstrated.

In this paper, a new technique based on particle swarm optimization (PSO) is proposed to incorporate voltage stability limits into traditional optimal power flow (OPF) formulations. Two objective functions such as fuel cost and voltage stability margin (loadability) are considered to be optimized. In proposed method systempsilas maximum loading margin (loadability) is calculated by continuation power flow (CPF). Particle swarm optimization (PSO) is employed to solve the proposed multi-objective OPF problem. The proposed method allows system operators in order to control the desired level of system security via adjusting the weighting factors of the different objective functions directly. Simulation results for the IEEE 30-bus test system demonstrate the effectiveness of the PSO in solving multi-objective OPF problems.

This paper presents optimality criteria for fuzzy-valued fractional multi-objective optimization problem. There are numerous optimality criteria which have been established for the deterministic fractional multi-objective optimization problems. Very few studies are available on the establishment of optimality criteria for fuzzy-valued multi-objective optimization problem. So, Karush–Kuhn–Tucker optimality criteria for fuzzy-valued fractional multi-objective problem are established by using Lagrange multipliers. First, the original problem is modified using the parametric approach of Dinkelbach into multi-objective non-fractional optimization problem, and then, the optimality conditions are established for the modified problem using the Hukuhara derivative. The established optimality criteria are verified by two numerical examples.

This paper proposed a new method for humanoid robot gait generation based on several objective functions. The proposed method is based on multiobjective evolutionary algorithm. In our work, we considered two competing objective functions: MCE and MTC. Based on simulation and experimental results, we conclude: Multiobjective evolution is efficient because optimal humanoid robot gaits with completely different characteristics can be found in one simulation run. The nondominated solutions in the obtained Pareto-optimal set are well distributed and have satisfactory diversity characteristics. The optimal gaits generated by simulation gave good performance when they were tested in the real hardware of "Bonten-Maru" humanoid robot. The optimal gait reduces the energy consumption and increases the stability during the robot motion. In the future, it will be interesting to investigate if the robot can learn in real time to switch between different gaits based on the environment conditions. In uneven terrains MTC gaits will be more

This work establishes a solution to assess the bus loadability limits by using a multi-objective optimization evaluation of active and reactive power. As a part of the methodology, it is also considered the operating limits of the Electric Power Systems (EPS), such as the generation capacity, reactive power reserves, active power capacity, restrictions of the transmission system, and bus voltages. The Pareto front is obtained from the multi-objective problem, represented by the bus's PQ curve under analysis. The multiobjective optimization problem is solved by combining two functions and the limits of active and reactive power margins at the same bus. Complementary, the methodology is applied to the IEEE 14 Bus system to evaluate its applicability, analyze the results, and evidence its strengths. Thus, a reliable method is provided to assess the bus's power margins, the system's security in electrical networks, and the EPS loadability limits.

In electricity market, both power system safety and cost need to be considered during the electricity trading. So the multi-objective optimal power flow (MOOPF) mathematical model is formulated for the power system with reactive power cost, which contains various operational constraints, power system equation and inequality constraints. In addition, a modified sooty tern optimization algorithm (MSTOA) is proposed to solve the MOOPF problem, which fully validated on the IEEE30 test system. Moreover, a constrained prior strategy based on Pareto-dominant (CPSP) is proposed for solving the inequality constraints on state variables. The simulation results show that MSTOA appears to find the best compromise solution (BCS) with higher feasibility and the Pareto front distribution of the obtained solution set is more uniform. Finally, the proposed MSTOA can provide more and better economic solutions for decision-makers while facing the MOOPF problem in the electricity trading.

Application of modified pigeon-inspired optimization algorithm and constraint-objective sorting rule on multi-objective optimal power flow problem

In September 2023, the Singapore Ministry of Health revised the national personal protective equipment (PPE) guideline for health care personnel (HCP) attending to suspected or confirmed patients with COVID-19, recommending the use of N95 respirators alone. However, data on the associations between PPE deescalation, staff COVID-19 incidence, and sustainability outcomes are limited. To evaluate the associations between PPE deescalation guidelines on staff COVID-19 incidence and the environmental sustainability outcomes of the measure. This was a retrospective quality improvement study of hospital PPE usage using data from Tan Tock Seng Hospital and the National Centre for Infectious Diseases (NCID), analyzing monthly COVID-19 incidence rates among HCP (all medical staff, allied health care workers, ancillary staff, and administrative staff from the hospital campus) and in the community over 12 months before and after PPE deescalation measures (October 2021 to September 2022 and October 2022 to September 2023). COVID-19 incidence rates among HCP and in the community; economic and environmental outcomes of removing single-use gowns from routine COVID-19 care, including reductions in carbon footprint, plastic waste generation, and cost savings. The mean (SD) monthly number of HCP was 10 774 (79) (range, 10 636-10 891) preimplementation and 11 099 (200) (range, 10 864-11 449) postimplementation. Our analysis revealed PPE deescalation was not associated with an increase in monthly COVID-19 infections among hospital staff, with the trends aligning with population infection rates. The median (IQR) staff COVID-19 infection rate relative to the community COVID-19 infection rate was 2.6 (1.9-3.6) preimplementation compared with 1.5 (0.9-3.1) postimplementation. An estimated 4 gowns per patient-day were saved, totaling 440 532 gowns over 12 months. This equated to an estimated reduction in health care costs by SGD 453 748 (approximately USD 333 970) and reductions of 398 681.46 kg carbon dioxide equivalent in carbon emissions and 66 080 kg of plastic waste. This quality improvement study of hospital PPE usage observed that the national PPE deescalation guidelines corresponded with the reductions in protective gown use, associated costs, carbon footprint, and plastic waste generation with no apparent compromise to staff safety and health.

The increasing capacity of nuclear units in power grid poses threat to system stability and security. Load disturbance may cause overspeed of the units and trigger the overspeed protection controller (OPC). Repeating actions of valves have a strong impact on the security of the equipment and may result in a collapse of the power system. Based on the analysis of frequency regulation, mathematic models for nuclear units in the grid are established in this paper to simulate the dynamic process when load disturbance occurs. The critical action of the OPC is proposed as the constraint of safe operation for the units. The relationship between different styles of frequency regulation and the allowable capacity of the nuclear units in the grid is discussed. This research can help to estimate the capacity limitation of nuclear units in the grid.

ABSTRACTProbabilistic and non-probabilistic methods have been proposed to deal with design problems under uncertainties. Reliability-based design and robust design are probabilistic strategies traditionally used for this purpose. In the present contribution, reliability-based robust design optimization (RBRDO) is formulated as a multi-objective problem considering the interaction of both approaches. The proposed methodology is based on the differential evolution algorithm associated with two strategies to deal with reliability and robustness, respectively, namely inverse reliability analysis and the effective mean concept. This multi-objective optimization problem considers the maximization of reliability and robustness coefficients as additional objective functions. The effectiveness of the methodology is illustrated by two classical test cases and a rotor-dynamics application. The results demonstrate that the proposed methodology is an alternative method to solve RBRDO problems.

In many optimization problems, the main goal is to improve a single performance index in which a minimum or maximum value of this index fully reflects the quality of the response obtained from a system. However, in some cases, it is impossible to rely solely on a single index, so a multi-objective optimization problem with multiple performance indicators is considered where the values of all of them should be optimized simultaneously. The mentioned process requires a multi-objective optimization algorithm that can deal with the complexity of problems with simultaneous indexes. This paper presents the multi-objective version of a recently proposed metaheuristic algorithm called Crystal Structure Algorithm (CryStAl) which was inspired by the principles underlying the formation crystal structures. For the performance evaluation of this algorithm which is called MOCryStAl, the benchmark problems of the Completions on Evolutionary Computation (CEC) on multi-objective optimization, called CEC-09, are utilized. Some real-world engineering design problems are used to evaluate the efficiency of the proposed approach. The results demonstrate that the proposed methods can provide excellent results in dealing with the considered multi-objective problems.

In this paper, we perform some computational experiments on the new global scalarization method for multi-objective optimization problems. Its main idea is to construct, for a given multi-objective optimization problem, a global scalarization function whose values are non-negative real numbers. The points where the scalarization function attains the zero value are exactly weak Pareto stationary points for the original multi-objective problem.