Multiobjective mix generation planning considering utility-scale solar PV system and voltage stability: Nigerian case study

Adewuyi, Oludamilare Bode; Shigenobu, Ryuto; Senjyu, Tomonobu; Lotfy, Mohammed Elsayed; Howlader, Abdul Motin

doi:10.1016/j.epsr.2018.12.010

Cited by 39 publications

(31 citation statements)

References 18 publications

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“…The Nigerian 330/132/33 kV, 28‐bus network has been extensively described in other works . The system has a combined average available generation capacity of 6159.60 MW, as shown in Table .…”

Section: Simulation Condition and Results Discussionmentioning

confidence: 99%

“…The Nigerian 330/132/33 kV, 28-bus network has been extensively described in other works. 59,60 The system has a combined average available generation capacity of 6159.60 MW, as shown in Table 5. Based on the available report in literature, an average of 23 occurrences of system collapse, both partial and total, is experienced yearly on the Nigerian power system.…”

Section: Nigerian 28 Bus Systemmentioning

confidence: 99%

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Comparative analysis of a new VSC‐optimal power flow formulation for power system security planning

Adewuyi

Howlader

Olaniyi

et al. 2020

Int Trans Electr Energ Syst

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Summary The performance of a new approach for multiobjective voltage stability constrained‐optimal power flow (VSC‐OPF) formulation is compared with a conventional VSC‐OPF approach for improving the steady‐state power systems security in this paper. The compared OPF problems involve the minimization of the fuel cost and minimization of novel line stability index (NLSI) for case 1 and minimization of fuel cost and maximization of line static stability margin (CBI) for the proposed case 2. Clerc's constricted PSO algorithm modified with the nondominated sorting algorithm is used for obtaining the best solution point for each of the decision variables. Network security constraints and bus voltage limits are considered along with the constraint on permissible line stability margin limit adopting the quadratic penalty model for constraints violations. The approach is tested on a load‐modified IEEE 30‐bus and actual Nigerian 28‐bus systems. From the results obtained, the proposed OPF formulation performed better for simultaneous consideration of voltage stability improvement and line loss reduction.

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Section: Simulation Condition and Results Discussionmentioning

confidence: 99%

Section: Nigerian 28 Bus Systemmentioning

confidence: 99%

Comparative analysis of a new VSC‐optimal power flow formulation for power system security planning

Adewuyi

Howlader

Olaniyi

et al. 2020

Int Trans Electr Energ Syst

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“…The performance of the power system in steady-state operating condition based on Newton-Raphson power flow solution is represented by the Jacobian matrix connection between the change in bus power and voltage parameters as given below [24]:…”

Section: Electricity Market Deregulation and Available Transfer Capacmentioning

confidence: 99%

“…The critical transactions, corresponding ATC limiting lines, CBI and CBI limiting line are presented in Table 5. The most critical transactions is T32 (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24) with the least ATC value of 0.0099 MW, the ATC limiting line is L62 (B48-B49), and the minimum CBI value is 0.1136 pu (11.36 MW) for the CBI limiting line L36 (B24-B25). The optimization procedure is initiated for the simultaneous maximization of the least ATC and least CBI using the weighted sum approach using TVAC-PSO and the convergence characteristics is shown in Figure 8.…”

Section: Ieee 57 Bus Systemmentioning

confidence: 99%

Voltage Security-Constrained Optimal Generation Rescheduling for Available Transfer Capacity Enhancement in Deregulated Electricity Markets

et al. 2019

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Modern utilities are forced to operate very close to their loadable limits (maximum capacity) due to geographical, economical and some technical reasons. The deregulation of the power industry, the competitive nature of modern electricity markets and the continuous quest for modernization of cities and hamlets all over the world has also led to fast increase in the load demand. The stability of power systems all over the world are threatened with recurrent occurrences of voltage stability issues. Hence, Inter-zonal energy transactions between willing supplier and buyers need to be done with adequate consideration for power system security. In this work, a voltage security-constrained optimal generator active and reactive power rescheduling is carried out using the IEEE 30 and IEEE 57 bus systems. The simultaneous maximization of available transfer capacity (ATC) and voltage stability margin (VSM), using the weighted sum approach, is the objective function. Credible optimal power flow and power system security constraints are considered. Three variants of particle swarm optimization in MATLAB® are used in this work for analyzing the results for objectivity. The technical and economic benefits of the optimal generator rescheduling on the system’s ATC, VSM, line losses, line flow and voltage profile are adequately analyzed.

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“…WL Theo et al [22], also presented a review on system planning and optimization techniques applicable to hybrid energy systems integration whilst comparing various mathematical programming methods. Reference [23] used MOPSO algorithm to compute sufficient capacity of solar PV that the power system can accommodate while maintaining the voltage stability of the system. The literature [24] also applied the MOPSO technique to optimally size grid-connected ground mounted and rooftop PV system in Freetown, Sierra Leone, to reduce the daily generation cost and gap between generation and demand.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Criteria Decision Maker for Grid-Connected Hybrid Renewable Energy Systems Selection Using Multi-Objective Particle Swarm Optimization

et al. 2019

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Combating climate change issues resulting from excessive use of fossil fuels comes with huge initial costs, thereby posing difficult challenges for the least developed countries in Sub-Saharan Africa (SSA) to invest in renewable energy alternatives, especially with rapid industrialization. However, designing renewable energy systems usually hinges on different economic and environmental criteria. This paper used the Multi-Objective Particle Swarm Optimization (MOPSO) technique to optimally size ten grid-connected hybrid blocks selected amongst Photo-Voltaic (PV) panels, onshore wind turbines, biomass combustion plant using sugarcane bagasse, Battery Energy Storage System (BESS), and Diesel Generation (DG) system as backup power, to reduce the supply deficit in Sierra Leone. Resource assessment using well-known methods was done for PV, wind, and biomass for proposed plant sites in Kabala District in Northern and Kenema District in Southern Sierra Leone. Long term analysis was done for the ten hybrid blocks projected over 20 years whilst ensuring the following objectives: minimizing the Deficiency of Power Supply Probability (DPSP), Diesel Energy Fraction (DEF), Life Cycle Costs (LCC), and carbon dioxide (CO 2 ) emissions. Capacity factors of 27.41 % and 31.6 % obtained for PV and wind, respectively, indicate that Kabala district is the most feasible location for PV and wind farm installations. The optimum results obtained are compared across selected blocks for DPSP values of 0–50% to determine the most economical and environmentally friendly alternative that policy makers in Sierra Leone and the region could apply to similar cases.

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Multiobjective mix generation planning considering utility-scale solar PV system and voltage stability: Nigerian case study

Cited by 39 publications

References 18 publications

Comparative analysis of a new VSC‐optimal power flow formulation for power system security planning

Comparative analysis of a new VSC‐optimal power flow formulation for power system security planning

Voltage Security-Constrained Optimal Generation Rescheduling for Available Transfer Capacity Enhancement in Deregulated Electricity Markets

A Multi-Criteria Decision Maker for Grid-Connected Hybrid Renewable Energy Systems Selection Using Multi-Objective Particle Swarm Optimization

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