Multi-area economic dispatch with reserve sharing using dynamically controlled particle swarm optimization

Jadoun, Vinay Kumar; Gupta, Nikhil; Niazi, K. R.; Swarnkar, Anil

doi:10.1016/j.ijepes.2015.06.008

Cited by 35 publications

(13 citation statements)

References 32 publications

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“…In contrast to the traditional approach that the TUs with no DPZs were managed to corporate in up and down spinning reserve requirements [33], [34], constraints (16) and (17) illustrate that all the TUs are able to provide the 10-min up/down reserves for which the up and down ramping rates are rationally modeled as As shown, the 10-min up and down reserve capabilities of each TU shrink to its sub-feasible region to avoid the load regulation from DPZs. The spinning reserve satisfaction with and without considering the DPZs for a typical TU is demonstrated in Fig.…”

Section: Problem Formulationmentioning

confidence: 98%

“…Prune the repository using ( [20] 129,911.8 NA NA 160.5300 ABCO [20] 124,009.4 NA NA 126.9300 EP [6] 123,591.9 NA NA NA DCPSO [16] 121,948.8 NA NA 64.8000 DE [20] 121,794.8 NA NA NA TLBO [19] 121,760.5 NA NA NA CGBABC [23] 121 …”

Section: Referencesmentioning

confidence: 99%

“…In addition, multi‐objective particle swarm optimisation (MOPSO) [13], θ ‐improved cuckoo optimisation algorithm [14], and modified cuckoo search algorithm and differential evolution [15] are proposed to apply to the BED optimisation problems. The dynamically controlled PSO (DCPSO) [16], harmony search (HS) [17], an improved PSO [18], teaching‐learning‐based optimisation (TLBO) [19], artificial bee colony optimisation (ABCO) [20], and PSO [21] are developed to handle the MED problems. An improved differential evolution [22], chaotic global best artiﬁcial bee colony (CGBABC) [23], and PSO [24] are also developed to handle the MBED challenges.…”

Section: Introductionmentioning

confidence: 99%

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Practical multi‐area bi‐objective environmental economic dispatch equipped with a hybrid gradient search method and improved Jaya algorithm

Azizipanah‐Abarghooee

Dehghanian

Terzija

2016

IET Generation, Transmission & Distribution

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Abstract:Generation dispatch decision making integrated into a centralized wholesale electricity market has been always a challenging concern for transmission system operators and has progressively received significant attention in recent years. The reason lies mainly in the complex and highly-interconnected structure of the grid, escalated demand, and the crisis of the amount and price of energy. For the dispatch solutions to be highly reliable with an acceptable level of accuracy, several factors of notable importance need to be well taken into account, among which one can mention the valve-point effect, multiple fuel options and disjoint prohibited zones, up/down ramp rate requirements of generating units, as well as the spinning reserve constraints which, in turn, intensify the non-smoothness, nonconvexity, non-linearity, and dynamic restriction of such combinatorial hard problems. This paper proposes a new optimization toolset for the bi-objective multi-area economic dispatch problem to determine the transmission interface power flow and power output of generating units while satisfying system demand and security constraints at each area. The proposed architecture builds upon an improved gradient-based Jaya algorithm to generate a feasible set of Pareto optimal solutions corresponding to the operation cost and fuel combustion emission calculated through a new robust bi-objective gradient based method. The projected algorithm is proved to be capable of finding the robust global or near-global Pareto solutions fast and accurate.Keywords: Bi-objective gradient-based technique; economic dispatch; environmental; heuristic; improved Jaya algorithm, optimization. E-mail:rasoul. obj, tObjective function and time step indices, respectively. Disjoint prohibited zone (DPZ) index. B. Constants, , , TiePower flow from area k to k' at time t.

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Section: Problem Formulationmentioning

confidence: 98%

Section: Referencesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Practical multi‐area bi‐objective environmental economic dispatch equipped with a hybrid gradient search method and improved Jaya algorithm

Azizipanah‐Abarghooee

Dehghanian

Terzija

2016

IET Generation, Transmission & Distribution

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“…The former Pbest means the position with the best fitness searched by individual particle while the latter Gbest refers to the position with the best fitness found by all particles. Every time the particle updates its position, the fitness is recalculated and the positions of Pbest and Gbest are updated by comparing the fitness of new particle with that of the individual extremum and group extremum [10].…”

Section: Basic Principlesmentioning

confidence: 99%

A cooperative particle swarm optimization with constriction factor based on simulated annealing

Zhang

Wang

2018

Computing

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As many engineering optimization problems are rather complicated, it is usually necessary to search the optimal solution in a complex and huge search space. When faced with these large-scale problems, conventional optimization algorithms need to traverse the entire search space and it is impossible for them to finish the search within polynomial time. Moreover, it can't meet requirements in terms of computation velocity, convergence and sensitivity to initial value. So, it is very difficult to apply them to engineering optimization problems. Swarm intelligence methods simulate the collective behaviors of social creatures in the nature and they come from the relationship between the community formed by simple individuals and the environment as well as the interactions between the individuals. A single individual can only perform simple tasks, but the population formed by single individuals can fulfill complex tasks. Such intelligence presented by such population is called swarm intelligence. Due to the limitations of existing optimization algorithms, it is usually impractical to obtain excellent computational performance with only one optimization algorithm. In consideration of the jumping property of simulated annealing, it is not easy to get trapped into local minimum and it has strong local search capability near the optimal value and fast convergence velocity. This paper combines it with particle swarm optimization, proposes a cooperative particle swarm optimization with constriction factor based on simulated annealing (SA-CPSO), offers guidelines on selection of related parameters and dynamically adjusts the particle velocity according to its movement track. In this way, it improves the convergence velocity of the algorithm by improving the spatial search ability of the particle so as to make the particle accept the solution which makes the fitness of the objective function "better" as well as the solution that makes the said fitness "worse" at a certain probability during the flight of the particle. The experiment shows that the SA-CPSO improves the diversity of the particle and enhances its ability to get rid of locally optimal solutions. So, SA-CPSO is not easy to be trapped into local optimum and it has stronger ability of global optimization, a faster convergence velocity and higher convergence accuracy.

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“…max min Tjm Tjm Tjm P P P ≤ ≤ (5) There is a constraint exists in multi-area economic dispatch, which is known as area power balance constraint. This constraint requires all the generation units in jth area to generate enough power to cater the load demand in the area P Dj and the power, which will flow through the tie-line from jth area to mth area P Tjm including the power losses at the tieline P Tljm [14]. Mathematically, this constraint is represented as follows:-…”

Section: A Problem Formulationmentioning

confidence: 99%

Multi-Area Economic Dispatch Performance Using Swarm Intelligence Technique Considering Voltage Stability

Zamani¹,

Musirin²,

Suliman³

et al. 2017

International Journal on Advanced Science, Engineering and Information Technology

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Economic dispatch is one of the important issues in power system operation and planning. Economic dispatch requires reliable technique to achieve minimal cost; otherwise, a non-optimal solution may cause non-economic electricity generation to the utility. Single area economic dispatch does not make complete electricity generation of the whole system on the electrical transmission network. Thus, multi-area economic dispatch implementation leads to complete consideration for power transmission system. This paper presents multi-area economic dispatch performance using swarm intelligence technique. In this study, swarm intelligence technique, namely the particle swarm optimization technique (PSO) is employed for solving multi-area economic dispatch problems. The algorithm is tested on a 2-area 48-bus power system with different case studies. Variation in active power loading in achieving an optimal solution is also considered in this study. Several trials were taken into consideration to assess the consistency of results. Comparative studies were performed with respect to evolutionary programming (EP) and revealed that PSO yields better results as compared to the EP.

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Multi-area economic dispatch with reserve sharing using dynamically controlled particle swarm optimization

Cited by 35 publications

References 32 publications

Practical multi‐area bi‐objective environmental economic dispatch equipped with a hybrid gradient search method and improved Jaya algorithm

Practical multi‐area bi‐objective environmental economic dispatch equipped with a hybrid gradient search method and improved Jaya algorithm

A cooperative particle swarm optimization with constriction factor based on simulated annealing

Multi-Area Economic Dispatch Performance Using Swarm Intelligence Technique Considering Voltage Stability

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