Combined heat and power (CHP) economic dispatch solved using Lagrangian relaxation with surrogate subgradient multiplier updates

Sashirekha, A.; Pasupuleti, Jagadeesh; Moin, Noor Hasnah; Sin, Tan Ching

doi:10.1016/j.ijepes.2012.07.038

Cited by 148 publications

(63 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the result comparison of load demands 2 and 3 in Tables 8 and 9, the proposed IPSO methods can obtain better total cost than MADS-LHS and MADS-PSO in [14] and the same total cost as that obtained from MADS-DACE [14], LR-SSMU-CSS [17], and LR-SSMU-SSBS [17]. For the computational time, the proposed methods are also vastly faster than other methods.…”

Section: Systems With Quadratic Fuel Cost Function Of Pure Power Unitsmentioning

confidence: 89%

“…The proposed improved PSO (IPSO) methods are tested on six systems and the obtained results are compared with those from other methods such as LR [4], GA [5], IACSA [6], EP [7], IGA-MU [8], LR-SQP [9], SARGA [10], BCO [11], HSA [1,[12][13], MADS-LHS [14], MADS-PSO [14], MADS-DACE [14], NDS [15], AIS [16], LR-SSMU-CSS [17], LR-SSMU-SSBS [17], TVAC-PSO [19], CGSO [20], and IGSO [20] in terms of cost and execution time.…”

Section: Constantmentioning

confidence: 99%

“…The best way to minimize the operating cost of the thermal units is to use both heat and electricity under working condition. Consequently, the optimal operation of combined heat and power units has played a very important role in enhancement of the e ciency of cial Immune System (AIS) [16], Lagrangian Relaxation with Surrogate Subgradient Multiplier Updates (LR-SSMU) [17], Selective Particle Swarm Optimization (SPSO) [18], Particle Swarm Optimization with Time Varying Acceleration Coe cients (TVAC-PSO) [19], Improved Group Search Optimization (IGSO) [20], and Oppositional Teaching Learning Based Method (OTLBO) [2]. Generally, the conventional methods such as Newton Method [3] and LR [4] su er diculty when dealing with the problems with nonconvex functions.…”

Section: Introductionmentioning

confidence: 99%

“…The NDS along with successive re nement search techniques have been employed to speed up the computation with low number of iterations and short computational time. In [17], the two proposed rules, i.e.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Improved Particle Swarm Optimization for Combined Heat and Power Economic Dispatch

Nguyen¹,

2016

Scientia Iranica

View full text Add to dashboard Cite

KEYWORDSAbstract. This paper presents eight Improved Particle Swarm Optimization (IPSO) algorithms for solving Combined Heat and Power Economic Dispatch (CHPED) problem with valve point loading e ects on fuel cost function and transmission losses. The main objective of the problem is to nd the power output and the heat output of available units so that the total fuel cost is minimized while satisfying power and heat demands and power and heat limits. The proposed IPSO algorithms are based on some modi cations on the parameters and the use of Cauchy distribution. The e ectiveness of the IPSO methods has been validated through six di erent test systems including three systems with quadratic fuel cost function of pure power units neglecting transmission losses, two systems with nonconvex fuel cost function of pure power units considering transmission losses, and one large-scale system with nonconvex fuel cost function of pure units and without transmission losses. The result comparisons between the IPSO methods and other methods in the literature have indicated that the proposed methods can obtain higher solution quality with faster computational time than many other methods. Therefore, the proposed IPSO methods would be very e cient methods for solving the nonconvex CHPED problem.

show abstract

Section: Systems With Quadratic Fuel Cost Function Of Pure Power Unitsmentioning

confidence: 89%

Section: Constantmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improved Particle Swarm Optimization for Combined Heat and Power Economic Dispatch

Nguyen¹,

2016

Scientia Iranica

View full text Add to dashboard Cite

show abstract

“…During these decades, there have been plentiful literature studies concerned with solving the combined heat and power economic dispatch problem (CHPEDP) in a centralized manner, such as the lambda iteration method [4], the gradient method [5], multi-team particle swarm optimization method [6], evolutionary method [7], etc. However, with the rapid development of the CHP system, centralized method has become the bottleneck of solving CHPEDP and cannot satisfy the requirement of practical applications.…”

Section: Introductionmentioning

confidence: 99%

Distributed Optimal Economic Dispatch Based on Multi-Agent System Framework in Combined Heat and Power Systems

Zhang

Huang

et al. 2016

Applied Sciences

View full text Add to dashboard Cite

Abstract:In this paper, a novel distributed method is presented to solve combined heat and power economic dispatch problem, which is formulated as a distributed coupled optimization problem. The optimization goal is achieved by establishing two modified consensus protocols with two corresponding feedback parts while satisfying the electrical and heat supply-demand balance. Moreover, an alternating iterative method is proposed to handle the heat-electrical coupling problem existed in the objective function and the feasible operating regions. In addition, the proposed distributed method is implemented by a multi-agent system framework, which only requires local information exchange among neighboring agents. Simulation results obtained on a 16-bus test system are provided to illustrate the effectiveness of the proposed distributed method.

show abstract

Leader‐distributed follower‐decentralized control strategy for economic dispatch in cascaded‐parallel microgrids

Han

Xia

Sun

et al. 2021

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

In this paper, we propose a leader-distributed follower-decentralized (LDFD) control strategy for cascaded-parallel microgrids (CPMGs) in the islanded mode, under which the economic dispatch (ED) is achieved with voltage quality guaranteed and frequency synchronization. In this control strategy, a distributed control method is presented for the leader of distributed generators (DGs) and an improved droop control method is designed for other DGs (followers) to optimize the generation cost and frequency control of the system. Furthermore, a unified voltage regulator is proposed to make the power factor of each DG in the same string equal. As only the leader in each string communicates with its neighboring leaders with low data transfer rate, very sparse low bandwidth communication networks are needed in this scheme, which reduces costs and increases the reliability in the event of a communication failure. In addition, the small-signal stability of the system is analyzed and the design of the related parameters is given. Some cases are designed to evaluate the performance of the strategy, and the simulation results verify the effectiveness of the proposed method. K E Y W O R D Scascaded-parallel microgrid, distributed control, economic dispatch, low bandwidth communication. | INTRODUCTIONMICROGRID is an effective way of integrating distributed generators (DGs), energy storage units via inverters, and local loads to achieve active distribution networks, 1 which can reduce the impact of large penetration of intermittent renewable energy integrated into the electrical grid, making the power system more reliable, safe, clean, and economical. In recent years, the growing penetration of DGs based on power electronic converters has brought structural changes 2 to microgrids to accommodate a wider range of applications. Combining the ideas of parallel-type 3 and series-List of abbreviations and symbols: LDFD, leader-distributed follower-decentralized; CPMG, cascaded-parallel microgrid; ED, economic dispatch; DG, distributed generator; EDP, economical dispatch problem; PCC, point of common coupling; ICR, incremental cost rate; DG#i.j, the j-th DG in String#i; V P , voltage amplitude of the common bus; δ p , power angle of the common bus; Y 0 L, amplitude of equivalent load admittance; φ L , angle of equivalent load admittance; V i.j , voltage amplitude of DG#i.j; δ i.j , phase angle of DG#i.j; λ i.j , incremental cost rate of DG#i.j; P i.j , output active power of DG#i.j; V G , set of nodes; A G , associated adjacency matrix; D in G , diagonal in-degree matrix; ω * , rated angular frequency; V * , rated voltage amplitude of common bus; Q i.j , output reactive power of DG#i.j; λ i:1 , estimate of the average ICR among leaders;P i:j , estimate of total active power of DGs in String#i; I l , identity matrix; H, ICR estimator transfer function matrix; A, system matrix.

show abstract

Combined heat and power (CHP) economic dispatch solved using Lagrangian relaxation with surrogate subgradient multiplier updates

Cited by 148 publications

References 16 publications

Improved Particle Swarm Optimization for Combined Heat and Power Economic Dispatch

Improved Particle Swarm Optimization for Combined Heat and Power Economic Dispatch

Distributed Optimal Economic Dispatch Based on Multi-Agent System Framework in Combined Heat and Power Systems

Leader‐distributed follower‐decentralized control strategy for economic dispatch in cascaded‐parallel microgrids

Contact Info

Product

Resources

About