A parallel multi-period optimal scheduling algorithm in microgrids with energy storage systems using decomposed inter-temporal constraints

Kim, Tae Hyun; Shin, Hyun-Suk; Kwag, Kyuhyeong; Kim, Wook

doi:10.1016/j.energy.2020.117669

Cited by 22 publications

(8 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In many applications, other parameters such as time are required as clustering parameters. At this time, the distance measurement needs to be appropriately improved [15][16].…”

Section: Advantages and Disadvantages Of K-means Clustering Algorithmmentioning

confidence: 99%

Energy Optimal Scheduling Problem Based on Clustering Algorithm

2021

View full text Add to dashboard Cite

The increasingly serious global energy crisis has provided important opportunities for the development of new energy. The concept of sustainable energy development advocated by the state has promoted the development and use of new energy, and a large number of new energy power generation units have been connected to the power system. K-means algorithm is a major breakthrough in the field of clustering analysis. Because of its convenience, it has been widely used in fraud detection, image processing, market analysis and other fields. In this paper, K-means algorithm is used for clustering mining of power grid data. In order to obtain more specific relationships between data, association rules mining based on Apriori algorithm is carried out for clustering results, and regions and systems with unreasonable power grid energy consumption are found; Finally, aiming at the problem area, a scheduling scheme is designed by using fuzzy control method to realize the optimal scheduling of power grid energy.

show abstract

“…In many applications, other parameters such as time are required as clustering parameters. At this time, the distance measurement needs to be appropriately improved [15][16].…”

Section: Advantages and Disadvantages Of K-means Clustering Algorithmmentioning

confidence: 99%

Energy Optimal Scheduling Problem Based on Clustering Algorithm

2021

View full text Add to dashboard Cite

show abstract

“…[28][29][30] Inertia weight is recommended to be linearly decreased with respect to iteration number. 27 For integer variables, namely the status of BES unit(s) at different time periods, at each iteration (20) to (21) are applied, then each variable is rounded to its nearest integer value. For binary decision variables, it is evident that flight Equations (20) and (21) are not suitable in their original form.…”

Section: Introductionmentioning

confidence: 99%

“…27 For integer variables, namely the status of BES unit(s) at different time periods, at each iteration (20) to (21) are applied, then each variable is rounded to its nearest integer value. For binary decision variables, it is evident that flight Equations (20) and (21) are not suitable in their original form. In conventional binary PSO, introduced in Reference 31 for binary decision variables, once the velocities are updated with Equation 20…”

Section: Introductionmentioning

confidence: 99%

“…32,33 In this paper, an improved strategy is used for dealing with binary decision variables, namely, the status of dispatchable DGs. In this strategy, once the velocities are updated with Equation (20), a quadratic transfer function as Equation 24is used to map the velocities into [0,1]. 13,33 α…”

Section: Introductionmentioning

confidence: 99%

“…In Reference 20, UC in EES‐integrated MG including CHP, PV and wind units has been formulated as a MINLP, while MG network constraints and reactive power dispatch have been considered and a parallel benders decomposition‐based optimisation algorithm has been used for solving the problem. However, the uncertainties of demand and renewable generation have not been considered.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An improved particle swarm optimisation for unit commitment in microgrids with battery energy storage systems considering battery degradation and uncertainties

Jordehi

2020

Int J Energy Res

View full text Add to dashboard Cite

Using electric storage systems (ESSs) is known as a viable strategy to mitigate the volatility and intermittency of renewable distributed generators (DGs) in microgrids (MGs). Among different electric storage technologies, battery energy storage (BES) is considered as the best option. In unit commitment (UC) module, the set of committed dispatchable DGs along with their power, power exported to/imported from macrogrid and status and power of ESS units are determined. In this paper, BES degradation is considered in UC formulation and an efficient particle swarm optimisation with quadratic transfer function is proposed for solving UC in BES-integrated MGs, while the uncertainties of demand, renewable generation and market price are considered and dealt with robust optimisation. UC is formulated as a multi-objective optimisation problem whose objectives are MG operation cost and BES degradation. The resultant multi-objective optimisation problem is converted into a singleobjective optimisation problem and the effect of weight factors on MG operation cost and BES lifecycle are investigated. The results show that by consideration of BES degradation in objective function, BES lifecycle increases from 350 to 500 and the minimum depth of charge increases from 5.5% to 34%; however, MG operation cost increases from $8717 to $8910.2. The results also show that by consideration of uncertainties, MG's operation cost increases by 8.22%.

show abstract