BMOTSM: design of a hybrid bioinspired model to determine optimal turbine sizing for capacity maximisation in environment-and-economy aware deployments

Figures & data

Figure 1. A typical wind farm repowering model based on bioinspired optimisation process.

Table 1. Complexity and space analysis of existing methods.

Parameter	SP SM (Xu, Geng, and Chu 2021)	HW GO FG (Tao and Kuenzel 2019)	BHO (Deveci 2020)	BM OT SM
Time complexity	O (N)	O (N)	O (N!)	O (log(N))
Space complexity	O (N^2)	O (N)	O (N)	O (log(N))

Figure 2. Overall flow of the proposed model for optimum generation locations and configurations.

Table 2. Locations used for simulation.

Name	Location	Stn.	Power (MW)
Bercha Wind Park	Ratlam	MP	50
Dangiri Wind Farm	Jaiselmer	RJ	54
Acciona Tuppadahalli	Chitra Durga District	KA	56.1
Welturi	Welturi	MAH	75
Jath	Jath	MAH	84
Damanjodi Wind Power Plant	Damanjodi	OD	99
Anantapur Wind Park	Nimba-gallu	AP	100
Mamatkheda Wind Park	Mamatkheda	MP	100.5
Vaspet	Vaspet	MAH	144
Vankusawade Wind Park	Satara District	MAH	259
Dhalgaon windfarm	Sangli	MAH	278
Brahmanvel windfarm	Dhule	MAH	528
Jaisalmer Wind Park	Jaisalmer	RJ	1064
Muppandal windfarm	Kanya Kumari	TN	1500

Table 3. Conversion efficiency levels for different wind farm optimisation models.

MaxG	CE (%) SPSM (Xu, Geng, and Chu 2021)	CE (%) HWGO FG (Bao 2019)	CE (%) BHO (Deveci 2020)	CE (%) BMO TSM
3	87.10	86.99	91.63	93.24
4	87.70	87.16	92.03	93.65
5	88.31	87.36	92.46	94.08
6	88.93	87.53	92.87	94.50
8	89.13	87.72	93.08	94.71
9	89.46	87.88	93.34	94.97
10	89.87	88.06	93.64	95.28
12	90.36	88.23	94.00	95.64
13	90.96	88.41	94.40	96.06
14	91.43	88.58	94.74	96.40
15	91.90	88.76	95.08	96.75
16	92.38	88.93	95.42	97.10
18	92.85	89.11	95.77	97.45
19	93.33	89.28	96.11	97.79
20	93.80	89.46	96.45	98.14
22	94.27	89.63	96.79	98.49
23	94.75	89.80	97.13	98.83
24	95.22	89.98	97.47	99.18
25	95.69	90.15	97.81	99.53

Figure 3. Conversion efficiency levels for different wind farm optimisation models.

Figure 4. Deployment cost needed for different wind farm optimisation models.

Table 4. Deployment cost needed for different wind farm optimisation models.

MaxG	DC (%) SPSM (Xu, Geng, and Chu 2021)	DC (%) HWGO FG (Tao and Kuenzel 2019)	DC (%) BHO (Deveci 2020)	DC (%) BMO TSM
3	77.42	71.01	79.67	62.16
4	77.96	71.15	80.03	62.43
5	78.50	71.31	80.40	62.72
6	79.04	71.45	80.75	63.00
8	79.22	71.61	80.93	63.14
9	79.52	71.74	81.16	63.32
10	79.88	71.88	81.43	63.52
12	80.32	72.02	81.74	63.76
13	80.85	72.17	82.09	64.04
14	81.27	72.31	82.38	64.27
15	81.69	72.45	82.68	64.50
16	82.11	72.60	82.98	64.73
18	82.53	72.74	83.27	64.96
19	82.96	72.88	83.57	65.20
20	83.38	73.02	83.87	65.43
22	83.80	73.17	84.17	65.66
23	84.22	73.31	84.46	65.89
24	84.64	73.45	84.76	66.12
25	85.06	73.60	85.06	66.35

Figure 5. Fragmentation percentage of soil due to wind farm optimisation models.

Table 5. Fragmentation percentage of soil due to wind farm optimisation models.

MaxG	FPS (%) SPSM (Xu, Geng, and Chu 2021)	FPS (%) HWGO FG (Tao and Kuenzel 2019)	FPS (%) BHO (Deveci 2020)	FPS (%) BMO TSM
3	40.98	39.45	42.72	30.99
4	41.15	39.50	42.84	31.07
5	41.31	39.60	42.97	31.17
6	41.89	39.66	43.31	31.42
8	42.03	39.78	43.44	31.52
9	42.16	39.84	43.54	31.59
10	42.27	39.92	43.64	31.66
12	42.41	40.00	43.76	31.74
13	42.77	40.08	43.99	31.91
14	43.00	40.16	44.15	32.03
15	43.22	40.24	44.31	32.15
16	43.45	40.32	44.47	32.26
18	43.67	40.39	44.63	32.38
19	43.89	40.48	44.79	32.49
20	44.12	40.55	44.95	32.61
22	44.34	40.63	45.11	32.73
23	44.56	40.71	45.27	32.84
24	44.79	40.79	45.43	32.96
25	45.01	40.87	45.59	33.07

Table 6. Cost to power ratio due to wind farm optimisation models.

MaxG	CPR (%) SPSM (Xu, Geng, and Chu 2021)	CPR (%) HWGO FG (Tao and Kuenzel 2019)	CPR (%) BHO (Deveci 2020)	CPR (%) BMO TSM
3	81.96	78.91	85.44	74.38
4	82.30	79.00	85.67	74.57
5	82.63	79.21	85.95	74.80
6	83.77	79.33	86.61	75.41
8	84.06	79.55	86.89	75.65
9	84.31	79.68	87.09	75.82
10	84.54	79.84	87.29	75.99
12	84.81	80.00	87.52	76.17
13	85.54	80.15	87.99	76.59
14	86.00	80.32	88.31	76.87
15	86.44	80.47	88.62	77.15
16	86.89	80.63	88.94	77.43
18	87.34	80.79	89.26	77.70
19	87.78	80.95	89.58	77.98
20	88.23	81.11	89.90	78.26
22	88.68	81.27	90.22	78.54
23	89.13	81.42	90.54	78.82
24	89.57	81.59	90.86	79.10
25	90.02	81.74	91.18	79.37

Figure 6. Cost to power ratio due to wind farm optimisation models.

Xu, Zhiwei, Hua Geng, and Bing Chu. 2021. “A Hierarchical Data-Driven Wind Farm Power Optimization Approach Using Stochastic Projected Simplex Method.” IEEE Transactions on Smart Grid 12 (4): 3560–3569. doi:10.1109/TSG.2021.3051773.

 Web of Science ®Google Scholar

Tao, Siyu, and Stefanie Kuenzel. 2019. “Optimal Micro-Siting of Wind Turbines in an Offshore Wind Farm Using Frandsen–Gaussian Wake Model.” IEEE Transactions on Power Systems : A Publication of the Power Engineering Society 34 (6): 4944–4954. doi:10.1109/TPWRS.2019.2916906.

 Web of Science ®Google Scholar

Deveci, Kaan. 2020. “Electrical Layout Optimization of Onshore Wind Farms Based on a Two-Stage Approach.” IEEE Transactions on Sustainable Energy 11 (4): 2407–2416. doi:10.1109/TSTE.2019.2957677.

 Web of Science ®Google Scholar

Bao, Weiyu. 2021. “A Hierarchical Inertial Control Scheme for Multiple Wind Farms with BESSs Based on ADMM.” IEEE Transactions on Sustainable Energy 12 (2): 751–760. doi:10.1109/TSTE.2020.2995101.

 Web of Science ®Google Scholar