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The economic emission dispatch problem (EEDP) is a nonconvex and nonsmooth multiobjective optimization problem in the power system field. Generally, fuel cost and total emissions of harmful gases are the problem objective functions. The EEDP decision variables are output powers of thermal generating units (TGUs). To make the EEDP problem more practical, valve point loading effects (VPLEs), prohibited operation zones (POZs), and power balance constraints should be included in the problem constraints. In order to solve this complex and constrained EEDP, a new multiobjective optimization technique combining the differential evolution (DE) algorithm and chaos theory is proposed in this study. In this new multiobjective optimization technique, a nondomination sorting principle and a crowding distance calculation are employed to extract an accurate Pareto front. To avoid being trapped in local optima and enhance the conventional DE algorithm, two different chaotic maps are used in its initialization, crossover, and mutation phases instead of random numbers. To overcome difficulties caused by the equality constraint describing the power balance constraint, a slack TGU is defined to compensate for the gap between the total generation and the sum of the system load and total power losses. Then, the optimal power outputs of all thermal units except the slack unit are determined by the suggested optimization technique. To assess the effectiveness and applicability of the proposed method for solving the EEDP, the six-unit and ten-unit systems are used. Moreover, obtained results are compared with other new optimization techniques already developed and tested for the same purpose. The superior performance of the ChMODE is also evaluated by using various metrics such as inverted generational distance (IGD), hyper-volume (HV), spacing metric (SM), and the average satisfactory degree (ASD).

The optimal reactive power dispatch problem optimizes the shunt capacitor bank installation in distribution systems, reducing power loss and also reducing the financial loss for the electricity market associated with power loss. Moreover, the sharing of both active and reactive power from different renewable energy sources like PV and wind in the form of distributed generation also contributes toward reducing power loss and improving the voltage profile of the system. But the installation and maintenance costs associated with these additional set-ups are rarely taken into consideration any optimization problem. This paper aims to reduce the power loss and improve the voltage profile of a radial distribution network with the integration of capacitor banks, PV, and wind energy sources, while taking into account the overall associated cost of each parameter during optimization. The problem is formulated as a novel cost minimization problem aiming to achieve the optimal settings for a life-long capacitor bank-PV-wind integrated distribution network with the least possible installation, operational, and maintenance costs while reducing its power loss significantly for a span of 20 years. The uncertain nature of PV and wind power output has been modeled using the beta probability distribution function and the Weibull probability distribution function, respectively. This unique proposed problem statement of the capacitor bank-PV-wind power integrated distribution network has been tested on the IEEE 33 and IEEE 141 bus systems and solved using the rock hyraxes swarm optimization (RHSO) algorithm. The results were compared with those from other nine well-established techniques, from which it was concluded that the RHSO algorithm has obtained optimal conditions for both systems to operate efficiently. The problem has also been tested on a practical 13-bus 33 kV distribution network in Maharashtra, India, to validate its performance on a practical system. The RHSO has successfully reduced the power loss to almost 17.48% w.r.t. the base case for the practical network while maintaining a minimum overall cost of $51,073,687.7582 for an entire life-span of 20 years.

Developing a low-carbon power system is critical and fundamental to cope with the challenges of global warming, in which the carbon capture and storage (CCS) technology will play a key role. In this study, the characteristics of energy flow and operation of carbon capture plants (CCPs) are clarified, while the mutual constraint between total generation output of CCPs and operation power consumption of carbon capture system is analysed. Then a generation output model and the optimal dispatch principle of CCPs is established, which can identify how the amount of carbon captured can represent a premium payment that can offset the increase in costs caused by the reduction on power output due to the CCS. On this basis, what with the low-carbon economy factors, a economic power dispatch model under low-carbon economy with CCPs considered is proposed. With the generation fuel cost and carbon emission cost incorporated in the objective function, the model proposed can effectively evaluate the power dispatch problem under low-carbon economy. Studies of the economic power dispatch of the 3-unit, 26-unit and 54-unit test systems show that the model proposed is effective and practical.

This paper proposes a new Enhanced Dwarf Mongoose Optimization Algorithm (EDMOA) with an alpha-directed Learning Strategy (LS) for dealing with different mathematical benchmarking functions and engineering challenges. The DMOA’s core concept is inspired by the dwarf mongoose’s foraging behavior. The suggested algorithm employs three DM social categories: the alpha group, babysitters, and scouts. The family forages as a team, with the alpha female initiating foraging and determining the foraging course, distance traversed, and sleeping mounds. An enhanced LS is included in the novel proposed algorithm to improve the searching capabilities, and its updating process is partially guided by the updated alpha. In this paper, the proposed EDMOA and DMOA were tested on seven unimodal and six multimodal benchmarking tasks. Additionally, the proposed EDMOA was compared against the traditional DMOA for the CEC 2017 single-objective optimization benchmarks. Moreover, their application validity was conducted for an important engineering optimization problem regarding optimal dispatch of combined power and heat. For all applications, the proposed EDMOA and DMOA were compared to several recent and well-known algorithms. The simulation results show that the suggested DMOA outperforms not only the regular DMOA but also numerous other recent strategies in terms of effectiveness and efficacy.

This article suggests a novel enhanced slime mould optimizer (ESMO) that incorporates a chaotic strategy and an elitist group for handling various mathematical optimization benchmark functions and engineering problems. In the newly suggested solver, a chaotic strategy was integrated into the movement updating rule of the basic SMO, whereas the exploitation mechanism was enhanced via searching around an elitist group instead of only the global best dependence. To handle the mathematical optimization problems, 13 benchmark functions were utilized. To handle the engineering optimization problems, the optimal power flow (OPF) was handled first, where three studied cases were considered. The suggested scheme was scrutinized on a typical IEEE test grid, and the simulation results were compared with the results given in the former publications and found to be competitive in terms of the quality of the solution. The suggested ESMO outperformed the basic SMO in terms of the convergence rate, standard deviation, and solution merit. Furthermore, a test was executed to authenticate the statistical efficacy of the suggested ESMO-inspired scheme. The suggested ESMO provided a robust and straightforward solution for the OPF problem under diverse goal functions. Furthermore, the combined heat and electrical power dispatch problem was handled by considering a large-scale test case of 84 diverse units. Similar findings were drawn, where the suggested ESMO showed high superiority compared with the basic SMO and other recent techniques in minimizing the total production costs of heat and electrical energies.

This paper proposes a novel nature-inspired optimization algorithm called the Fox optimizer (FOX) which mimics the foraging behavior of foxes in nature when hunting preys. The algorithm is based on techniques for measuring the distance between the fox and its prey to execute an efficient jump. After presenting the mathematical models and the algorithm of FOX, five classical benchmark functions and CEC2019 benchmark test functions are used to evaluate it’s performance. The FOX algorithm is also compared against the Dragonfly optimization Algorithm (DA), Particle Swarm Optimization (PSO), Fitness Dependent Optimizer (FDO), Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), Chimp Optimization Algorithm (ChOA), Butterfly Optimization Algorithm (BOA) and Genetic Algorithm (GA). The results indicate that FOX outperforms the above-mentioned algorithms. Subsequently, the Wilcoxon rank-sum test is used to ensure that FOX is better than the comparative algorithms in statistically significant manner. Additionally, parameter sensitivity analysis is conducted to show different exploratory and exploitative behaviors in FOX. The paper also employs FOX to solve engineering problems, such as pressure vessel design, and it is also used to solve electrical power generation: economic load dispatch problems. The FOX has achieved better results in terms of optimizing the problems against GWO, PSO, WOA, and FDO.

This study proposes a cuckoo search algorithm (CSA) for solving non-convex economic dispatch (ED) considering generator and system characteristics including valve-point effects, multiple fuels, prohibited zones, spinning reserve and power loss. CSA is a new meta-heuristic optimisation method inspired from the obligate brood parasitism of some cuckoo species by laying their eggs in the nests of other host birds of other species. When the host birds discover an alien egg in their nest, they can either throw it away or simply abandon their nest and build a new one elsewhere. The CSA idealised such breeding behaviour in combination with Lévy flights behaviour of some birds and fruit flies for applying to various constrained optimisation problems. The effectiveness of the proposed method has been tested on different non-convex ED problems. Test results have indicated that the proposed method can obtain less expensive solutions than many other methods reported in the literature. Accordingly, the proposed CSA is a promising method for solving the practical nonconvex ED problems.

This study presents a real coded chemical reaction algorithm to solve economic load dispatch (ELD) problems involving different constraints such as power balance, ramp rate limits and prohibited operating zone constraints. Effects of valve-point loading and multi-fuel options of large-scale thermal plants are also studied. System transmission loss has also been considered in a few cases. Chemical reaction optimisation mimics the interactions of molecules in a chemical reaction to reach from a higher energy unstable state to a low energy stable state. A real coded version, known as real-coded chemical reaction optimisation is implemented here to solve ELD problems. The simulation results establish that the proposed approach outperforms several other existing optimisation techniques in terms of quality of solution obtained and computational efficiency. The results also prove the robustness of the proposed methodology to solve ELD problems.

Distributed energy technologies are gaining popularity nowadays; however, due to the highly intermittent characteristics of distributed energy resources, a larger penetration of these resources into the distribution grid network becomes of major concern. The main issue is to cope with the intermittent nature of the renewable sources alongside the requirements for power quality and system stability. Unlike traditional power systems, the control and optimization of complex energy systems comprising of wind, solar, thermal, and energy storage becomes difficult in many aspects, such as modelling, integration, operation, coordination and planning etc. This means that energy conversion as per the standards imposed by the energy market is unachievable without adequate control, management, and optimization. This edited book serves as a resource for the engineers, scientists and professionals working on distributed energy systems. The book is an extensive collection of state-of-the-art studies on advanced control paradigms for complex energy systems, with emphasis on the optimization and management of the high penetration of distributed energy resources into power distribution networks. Readers will find the book inspiring and useful whilst carrying out their own research in distributed energy systems. Key features: • An extensive collection of state-of-the-art studies on advanced control paradigms for complex energy systems. • Emphasis on the optimization and management of high penetration of distributed energy resources into power/energy distribution networks. • Serves as a valuable resource for engineers, scientists, academicians, experienced professionals, and research scholars who are working in management of energy systems.

This study introduces an enhanced self-adaptive wild goose algorithm (SAWGA) for solving economical-environmental-technical optimal power flow (OPF) problems in traditional and modern energy systems. Leveraging adaptive search strategies and robust diversity capabilities, SAWGA distinguishes itself from classical WGA by incorporating four potent optimizers. The algorithm's application to optimize an OPF model on the different IEEE 30-bus and 118-bus electrical networks, featuring conventional thermal power units alongside solar photovoltaic (PV) and wind power (WT) units, addresses the rising uncertainties in operating conditions, particularly with the integration of renewable energy sources (RESs). The inherent complexity of OPF problems in electrical networks, exacerbated by the inclusion of RESs like PV and WT units, poses significant challenges. Traditional optimization algorithms struggle due to the problem's high complexity, susceptibility to local optima, and numerous continuous and discrete decision parameters. The study's simulation results underscore the efficacy of SAWGA in achieving optimal solutions for OPF, notably reducing overall fuel consumption costs in a faster and more efficient convergence. Noteworthy attributes of SAWGA include its remarkable capabilities in optimizing various objective functions, effective management of OPF challenges, and consistent outperformance compared to traditional WGA and other modern algorithms. The method exhibits a robust ability to achieve global or nearly global optimal settings for decision parameters, emphasizing its superiority in total cost reduction and rapid convergence.