Analysis of Heuristic Optimization Technique Solutions for Combined Heat-Power Economic Load Dispatch

Singh N., Chakrabarti T., Chakrabarti P., Margala M., Gupta A., Krishnan S.B., Unhelkar B.

Most power is generated using fossil fuels like coal, natural gas, and diesel. The contribution of coal to power generation is very high compared to other sources. Almost all thermal power plants use coal as a fuel for power generation. Such sources of fossil fuels are limited and thus the cost of power generation increases. At the same time, the induced toxic gases due to these fossil fuels pollute the environment. The objective of this work is to solve the economic emission dispatch problem. Economic emission dispatch helps to find out how to operate power plants at the minimum cost and induce the minimum emissions at a thermal power plant. Economic emission dispatch with constraints is a nonlinear optimization problem. For the solution of such nonlinear economic emission load dispatch problems, this work considers a new particle swarm optimization technique. The proposed new PSO gives the best solution for economic emission load dispatch and handles the constraints. For the testing of the proposed new PSO algorithm, this work considered a case study of a system of six generating units, and it was tested for load demands of 700 MW, 800 MW, and 1000 MW. The results of the new PSO for the three load demands considered give the minimum generation cost, minimum emission, and minimum total cost compared to other optimization algorithms. The proposed techniques are effective, and they can help obtain the minimum generation cost and minimize emissions.

Singh N., Chakrabarti T., Chakrabarti P., Margala M., Gupta A., Praveen S.P., Krishnan S.B., Unhelkar B.

The fundamental objective of economic load dispatch is to operate the available generating units such that the needed load demand satisfies the lowest generation cost and also complies with the various constraints. With proper power system operation planning using optimized generation limits, it is possible to reduce the cost of power generation. To fulfill the needs of such objectives, proper planning and economic load dispatch can help to plan the operation of the electrical power system. To optimize the economic load dispatch problems, various classical and new evolutionary optimization approaches have been used in research articles. Classical optimization techniques are outdated due to many limitations and are also unable to provide a global solution to the ELD problem. This work uses a new variant of particle swarm optimization techniques called modified particle swarm optimization, which is effective and efficient at finding optimum solutions for single as well as multi-objective economic load dispatch problems. The proposed MPSO is used to solve single and multi-objective problems. This work considers constraints like power balance and power generation limits. The proposed techniques are tested for three different case studies of ELD and EELD problems. (1) The first case is tested using the data of 13 generating unit systems along with the valve point loading effect; (2) the second case is tested using 15 generating unit systems along with the ramp rate limits; and (3) the third case is tested using the economic emission dispatch (EELD) as a multi-objective problem for 6 generating unit systems. The outcomes of the suggested procedures are contrasted with those of alternative optimization methods. The results show that the suggested strategy is efficient and produces superior optimization outcomes than existing optimization techniques.

Ohaegbuchi D.N., Maliki O.S., Okwaraoka C.P., Okwudiri H.E.

Pawani K., Singh M.

Hosseini S.E., Najafi M., Akhavein A., Shahparasti M.

This paper presents an energy management method for the interconnected operation of power, heat, Combined Heat and Power (CHP) units to settle the Day-Ahead market in the presence of a demand response program (DRP). A major challenge in this regard is the price uncertainty for DRP participants. First, the definitive model of the problem is introduced from the perspective of the Regional Market Manager (RMM) in order to minimize the total supply cost in the presence of TOU program, which is a type of DRP. Furthermore, a market-oriented tensile model is presented in the form of a combination of over-lapping generations (OLG) and price elasticity (PE) formulations to determine the amount of electricity demand in the TOU program. Then, a price uncertainty model of the proposed problem is introduced according to the IGDT risk aversion and risk-taking strategies considering information gap decision theory (IGDT). The above problem is solved through the use of the co-evolutionary particle swarm optimization (C-PSO) algorithm and the proposed model is implemented on a standard seven-unit system for a period of 24 hours.

Shaheen A.M., Elsayed A.M., Ginidi A.R., EL-Sehiemy R.A., Alharthi M.M., Ghoneim S.S.

This paper proposes an improved marine predators’ optimization algorithm (IMPOA) for solving the combined heat and power (CHP) economic dispatch problem. This problem provides optimal scheduling of heat and power generation supplies and pursues to minimize the overall fuel cost (OFC) supply of cogeneration units considering their operational constraints. Four test systems are considered to check the performance of both the MPOA and the proposed IMPOA. The first test system is small sized which involve 5-unit, whereas the second system is medium sized which contains 48-unit system. The third and fourth test systems are large sized systems. The third test system includes 84-unit, which are divided into 40 power-only units, 20 heat only units, and 24 CHP units. The fourth test system includes 96-unit, which are divided into 52 power-only units, 20 heat-only units, and 24 CHP units. The obtained results clearly show the capability, efficiency, and feasibility of the IMPOA with respect to other relevant optimization techniques for optimal solutions of small, medium and large-scale systems. Additionally, the convergence characteristics of the proposed IMPOA are stable and the arrival of the optimal solution is faster than the conventional MPOA.

Goudarzi A., Fahad S., Ni J., Ghayoor F., Siano P., Haes Alhelou H.

The explosive demand for electricity and ecological concerns has necessitated the operation of power networks in a more cost-effective approach. In recent years, the integration of combined heat and power units has presented a potential answer to these problems; nevertheless, a new difficult challenge has emerged: finding an optimal solution for simultaneous dispatch of power and heat. Therefore, to tackle this problem, this work presents an intelligent sequential algorithm based on a hybridization of an enthusiasm-aided teaching and learning-based optimization algorithm (ETLBO) with an improved version of particle swarm optimization (IPSO). The proposed method can simultaneously minimize total generating costs while considering a variety of physical and operational limitations. In addition, this research designed an adaptive violation constraint management approach combined with the formulated hybridized optimization algorithm to ensure system constraints' safe preservation during the optimization process. Finally, the performance of the proposed method is compared to the recently developed metaheuristic algorithms as well as Knitro and IPOPT (industrially used optimization packages), in which the ETLBO-IPSO outperforms all the other methods.

Zou D., Gong D.

A differential evolution using migrated variables is proposed to deal with the combined heat and power dynamic economic dispatch problems in this paper. The new differential evolution improves the classical one from two aspects. Firstly, it incorporates an attracting factor involving direction information into the mutation operation, providing mutant vectors with more opportunities of searching potential regions. Secondly, it replaces a number of segments of the mutant vectors with those of previous target ones, contributing to improvements of candidates. Additionally, a method of repairing solutions is proposed to assist the solutions in moving towards the feasible regions rapidly. Each repaired solution can always satisfy four kinds of constraints including power generation limits, capacity limits of combined heat and power units, heat generation limits and ramp rate limits. In addition, it is likely to satisfy the other three kinds of constraints including prohibited operating zones, power balances and heat balances. As a result, the new differential evolution combined with the method of repairing solutions is able to accelerate the eliminations of constraint violations and the reduction of objective function value for each solution. Experimental results demonstrate that the new differential evolution can obtain desirable results and it outperforms the other five algorithms for the eight combined heat and power dynamic economic dispatch cases with different dimensions. • This paper focuses on the issue of combined heat and power dynamic economic dispatch. • A differential evolution based on migrating variables is proposed for the issue. • The improved differential evolution has strong convergence and high accuracy. • A method of repairing solutions is proposed to handle constraints efficiently. • Good solutions can be obtained by differential evolution and constraint handling.

Ginidi A., Elsayed A., Shaheen A., Elattar E., El-Sehiemy R.

This paper proposes a hybrid algorithm that combines two prominent nature-inspired meta-heuristic strategies to solve the combined heat and power (CHP) economic dispatch. In this line, an innovative hybrid heap-based and jellyfish search algorithm (HBJSA) is developed to enhance the performance of two recent algorithms: heap-based algorithm (HBA) and jellyfish search algorithm (JSA). The proposed hybrid HBJSA seeks to make use of the explorative features of HBA and the exploitative features of the JSA to overcome some of the problems found in their standard forms. The proposed hybrid HBJSA, HBA, and JSA are validated and statistically compared by attempting to solve a real-world optimization issue of the CHP economic dispatch. It aims to satisfy the power and heat demands and minimize the whole fuel cost (WFC) of the power and heat generation units. Additionally, a series of operational and electrical constraints such as non-convex feasible operating regions of CHP and valve-point effects of power-only plants, respectively, are considered in solving such a problem. The proposed hybrid HBJSA, HBA, and JSA are employed on two medium systems, which are 24-unit and 48-unit systems, and two large systems, which are 84- and 96-unit systems. The experimental results demonstrate that the proposed hybrid HBJSA outperforms the standard HBA and JSA and other reported techniques when handling the CHP economic dispatch. Otherwise, comparative analyses are carried out to demonstrate the suggested HBJSA’s strong stability and robustness in determining the lowest minimum, average, and maximum WFC values compared to the HBA and JSA.

Ginidi A.R., Elsayed A.M., Shaheen A.M., Elattar E.E., El-Sehiemy R.A.

Cogeneration systems economic dispatch (CSED) provides an optimal scheduling of heat/ power generating units. The CSED aims to minimize the whole fuel cost (WFC) of the cogeneration units taking into consideration their technical and operational limits. Then, the current paper examines the first implementation of dominant bio-inspired metaheuristic called heap-based optimization algorithm (HBOA). The HBOA is powered by an adaptive penalty functions for getting the optimal operating points. The HBOA is inspired from the organization hierarchy, where the mechanism consists of the interaction among the subordinates and their immediate boss, the interaction among the colleagues, and the employee's self-contribution. Based on the infeasible solutions' remoteness from the nearest feasible point, HBOA penalizes them with various degrees. Four case studies of the CSED are implemented and analyzed, which comprise of 4, 24, 84 and 96 generating units. The HBOA is proposed to solve CSED problem with consideration of transmission losses and the valve point impacts. An investigation with the recent optimization algorithms, which are supply demand optimization (SDO), jellyfish search optimization algorithm (JFSOA), and marine predators' optimization algorithm (MPOA), the improved MPOA (IMPOA) and manta ray foraging (MRF), is developed and elaborated. From the obtained results, it is clearly observed that the optimal solutions gained, in terms of WFC, reveal the feasibility, capability, and efficiency of HBOA compared with other optimizers especially for large-scale systems. case.

Chen X., Li K., Xu B., Yang Z.

Combined heat and power economic dispatch (CHPED) is an important optimization task in the economic operation of power systems. The interdependence of heat and power outputs of cogeneration units and valve-point effects of thermal units impose non-convexity, nonlinearity and complication in the dispatch modeling and optimization. In this paper, a novel PSO algorithm called biogeography-based learning particle swarm optimization (BLPSO) is applied to solve the CHPED problem considering various constraints including power output balance, heat production balance, feasible operation area of cogeneration unit and prohibited operation zones. In BLPSO, based on a biogeography-based learning model, each particle uses a migration operator to update itself based on the personal best position of all particles. This updating strategy helps BLPSO overcome premature convergence and improve solution accuracy. Moreover, a repair technique is employed to handle the system constraints and guide the solutions toward feasible zones. The effectiveness of the proposed method is evaluated by testing on four CHPED problems containing 5, 7, 24, and 48 units. The experimental results show that BLPSO outperforms the state-of-the-art methods in terms of solution accuracy and stability. Therefore, BLPSO can be regarded as a promising alternative for the CHPED problem. • BLPSO algorithm is applied for solving CHPED problem with various constraints. • The interdependence of heat and power outputs of cogeneration units impose great complication. • Non-convex CHPED problems with/without prohibited operating zones are considered. • Comprehensive simulation results demonstrate the effectiveness of the BLPSO algorithm.

Nazari-Heris M., Mohammadi-Ivatloo B., Zare K., Siano P.

Combined heat and power (CHP) technology can simultaneously satisfy heat and power loads. The objective of optimal production scheduling of CHP plants is finding optimal schedule of heat and power plants according to the constraints of network and component. In this research, the solution of CHP economic dispatch (CHPED) in large scale is investigated considering different scenarios. Firstly, the CHPED is tested on a 48-unit system to obtain minimum total operation cost, which includes the operation cost of thermal plants, CHP units and boilers, and the obtained optimal solutions are compared with recent publications. Then, a novel framework for a large-scale multi-zone CHPED problem is introduced, where each zone is responsible of providing the associated heat load. Finally, the multi-objective CHP dispatch problem is studied for handling two competing objectives consisting of operation cost and emissions of pollutant gases. The emission of pollutant gases includes the greenhouse gases emitted by thermal plants, CHP units and boilers. The model is tested on a three-zone 48-unit system for verifying the performance and effectiveness of the model. An annual cost saving of $1,939,534.08 can be attained by using the applied method for the 48-unit CHP system in comparison with the reported results in recent studies. • Proposing a novel multi-zone framework optimal scheduling of combined heat and power. • Studying optimal dispatch of CHP units in single-zone and multi-zone frameworks. • Investigating power transmission losses of the large-scale system using Kron’s model. • Multi-objective economic emission dispatch of CHP units using ε-constraint approach.

Srivastava A., Das D.K.

In this article, a new optimization technique known as Kho-Kho optimization (KKO) algorithm is presented. This proposed technique is a population based meta-heuristic method which is inspired from the strategies used by players in a well known tag-team game played in India, i.e. Kho-Kho. The performance and superiority of the proposed method with respect to other existing methods is evaluated using twenty nine benchmark functions and real-time optimization problems related to power system i.e. combined emission economic dispatch and combined heat and power economic dispatch problem.

Sundaram A.

This study implements a potent Multiobjective Multi-Verse Optimization algorithm to solve the highly complicated combined economic emission dispatch and combined heat and power economic emission dispatch problems. Solving these problems operates the power system integrated with cogeneration plants economically and reduces the environmental impacts caused by the pollutants of fossil fuel-fired power plants. A chaotic opposition based strategy is proposed to explore the search space extensively and to generate the initial populations for the multiobjective optimization algorithm. An effective constraint handling mechanism is also proposed to enable the population to remain within the bounds and in the feasible operating region of the cogeneration plants. The algorithm is applied to standard test functions, four test systems including a large 140 bus system considering valve-point effects, ramp limits, transmission power losses, and the feasible operating region of cogeneration units. The Pareto Optimal solutions obtained by the algorithm are well spread and diverse when compared with other optimization algorithms. The statistical analysis and various performance metrics used indicate the algorithm converges to true POF and is a viable alternative to solve the highly complicated combined economic emission dispatch and combined heat and power economic emission dispatch problems.

I. Alomoush M.

Song J., Zhang H., Zhang Y., Ma Z., He M.

López Hernández O., Romero Romero D., Badaoui M.

In this article, we present a modification to the Economic Dispatch (ED) model that addresses the non-convex nature of the cost curves associated with a Combined Cycle Power Plant (CCPP). Incorporating a binary variable provides greater precision in solving the combinatorial problem in only one simulation and, most importantly, demonstrates cost minimization among the three different cost curve models for dispatching the CCPP. Our results highlight the importance of considering different demand scenarios based on a reference forecast for one day ahead. Therefore, piecewise modeling is more feasible for solving the non-convex problem, showing greater accuracy regarding the operational state of the CCPP and avoiding the cost overestimation that occurs with traditional models. Moreover, it allows the operators to manage costs better and optimize generation potential, ultimately showing economic benefits for the system operator.

Mundotiya P., Shrimal S., Koli A., Kansotia G., Meena N., Tiwari H.

Singh H.P., Singh N., Mishra A., Sen S.K., Swarnkar M., Pandey D.

Singh N., Singh H.P., Mishra A., Khare A., Swarnkar M., Almas S.K.