Multi objective unit commitment with voltage stability and PV uncertainty

Furukakoi, Masahiro; Adewuyi, Oludamilare Bode; Matayoshi, Hidehito; Howlader, Abdul Motin; Senjyu, Tomonobu

doi:10.1016/j.apenergy.2018.06.074

Cited by 60 publications

(21 citation statements)

References 12 publications

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“…The voltage stability analysis is critical in the operation of electric power systems. In this regard, Furukakoi et al [14] used the Multi-Objective Genetic Algorithm (MOGA) for multi-purpose operation plan to achieve an improved voltage stability and reduce the PV output prediction error and it showed a 6% improvement in the voltage stability index. Moreover, Bode et al [54] used a multi-objective Particle Swarm Optimization (PSO) algorithm for optimal penetration of PV, while maintaining the system stability, and it showed an 11% improvement for the novel line stability index.…”

Section: Resultsmentioning

confidence: 99%

Optimal Design of Hybrid PV-Battery System in Residential Buildings: End-User Economics, and PV Penetration

et al. 2019

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This paper proposes an optimal design for hybrid grid-connected Photovoltaic (PV) Battery Energy Storage Systems (BESSs). A smart grid consisting of PV generation units, stationary Energy Storage Systems (ESSs), and domestic loads develops a multi-objective optimization algorithm. The optimization aims at minimizing the Total Cost of Ownership (TCO) and the Voltage Deviation (VD) while considering the direct and indirect costs for the prosumer, and the system stability with regard to intermittent PV generation. The optimal solution for the optimization of the PV-battery system sizing with regard to economic viability and the stability of operation is found while using the Genetic Algorithm (GA) with the Pareto front. In addition, a fuzzy logic-based controller is developed to schedule the charging and discharging of batteries while considering the technical and economic aspects, such as battery State of Charge (SoC), voltage profile, and on/off-peak times to shave the consumption peaks. Thus, a hybrid approach that combines a Fuzzy Logic Controller (FLC) and the GA is developed for the optimal sizing of the combined Renewable Energy Sources (RESs) and ESSs, resulting in reductions of approximately 4% and 17% for the TCO and the VD, respectively. Furthermore, a sensitivity cost-effectiveness analysis of the complete system is conducted to highlight and assess the profitability and the high dependency of the optimal system configuration on battery prices.

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Section: Resultsmentioning

confidence: 99%

Optimal Design of Hybrid PV-Battery System in Residential Buildings: End-User Economics, and PV Penetration

et al. 2019

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“…It was shown that such voltage deviations can be drastically reduced to 71% by smoothing the fluctuating generation of PV thanks to the usage of energy storage systems (ESSs). In [9], the authors studied operation planning methods by taking into account demand-response programs and PV-battery systems to maximize the stability of the voltage and minimize PV generation forecasting errors and operational costs. Certain other contributions go one step further, and in addition to considering the technical aspects, they analyze the economic part of the derived solution(s).…”

Section: Related Workmentioning

confidence: 99%

“…P D (k) = B * α d / / discharge the battery with the maximum discharging rate9:E(k + 1) = E(k) − P H (k).T u / /update the state of charge of the battery 10: C H (k cp )− = P H (k).T u 11: else 12: P C (k) = B * α c / / charge the battery with the maximum charging rate and cheapest price 13: E(k + 1) = E(k) + P C (k).T u / / update the state of charge of the battery 14: P G (k) = P H (k) / / serve the household demand from the grid 15: K cp = K cp \{k cp } / / remove from the set K cp the cheapest timeslot k cp 16: k cp = CheapestTimeslot(K cp ) / / next cheapest timeslot k cp such that k cp > k 17: C H (k cp ) = ∑ k cp−1 i=k+1 P H (i).T u / / energy consumption until the new cheapest timeslot 18: k) = P H (k) / / serve the household demand from the grid 21: P C (k) = B + α c / / charge the battery with the maximum charging rate 22:…”

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confidence: 99%

Optimized Charging of PV-Batteries for Households Using Real-Time Pricing Scheme: A Model and Heuristics-Based Implementation

Basmadjian

2020

Electronics

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The combination of solar panels (PV) with energy storage systems (ESS) has been becoming more and more a common practice for households. In this context, the battery of ESS satisfies the needs of the household when PV generation is not present. Recently, dynamic pricing became one of the measures taken to shift the demand. Thanks to technological advances (e.g., smart meters), real-time pricing (RTP) has shown to be the most attractive option in the market, due to the ease of estimating price elasticity over various time periods. We studied a PV-battery system for the case of households which are under RTP scheme. To this end, we described and modeled the underlying system, and compiled an objective function having as an optimization goal, the minimization of the charging cost of the battery. Furthermore, we propose a heuristics-based algorithm that schedules the charging process during cheap periods. To evaluate the amount of savings, we considered a real-life testbed and implemented the proposed algorithm by taking into account different scenarios. The results demonstrate the benefits of households adhering to real-time pricing scheme, where the savings reached 50% in certain cases.

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“…Most of the multiobjective UC problems are formulated as an extension of the stochastic UC problems for the simultaneous realization of more than one objective of system operators. A novel multipurpose operation planning method for minimizing the prediction error of power generated from solar PV generators to achieve the optimal reduction of the operating cost and improve the voltage stability of power systems, simultaneously, was reported in [59]. An optimally scheduled demand response (DR) program and properly sized storage system are considered as the main parameters for voltage stability improvement and PV output prediction error minimization.…”

Section: Multiobjective Uc Problemmentioning

confidence: 99%

“…The UC problem considers a 138-MW ESS system with 1192-MWh capacity for leveling the load demand, and it considers ESSs constraints such as SOC constraint, Equation (50); maximum, and minimum limits, Equation (51); maximum charge constraint, Equation (52); minimum charge constraint, Equation (53); discharge power rating, Equation (54); disable simultaneous charging and discharging, Equation (55); charge ramp-up, Equation (56); charge ramp-down, Equation (57); discharge ramp-up, Equation (58); and discharge ramp-down, Equation (59). Figure 1 shows that the UC problem without considering the ESS system involves turning on all the 10 TG units, as shown in Figure 2, in order to meet the load demand.…”

Section: Ess With Uc Programmentioning

confidence: 99%

Energy Storage System Analysis Review for Optimal Unit Commitment

et al. 2019

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Energy storage systems (ESSs) are essential to ensure continuity of energy supply and maintain the reliability of modern power systems. Intermittency and uncertainty of renewable generations due to fluctuating weather conditions as well as uncertain behavior of load demand make ESSs an integral part of power system flexibility management. Typically, the load demand profile can be categorized into peak and off-peak periods, and adding power from renewable generations makes the load-generation dynamics more complicated. Therefore, the thermal generation (TG) units need to be turned on and off more frequently to meet the system load demand. In view of this, several research efforts have been directed towards analyzing the benefits of ESSs in solving optimal unit commitment (UC) problems, minimizing operating costs, and maximizing profits while ensuring supply reliability. In this paper, some recent research works and relevant UC models incorporating ESSs towards solving the abovementioned power system operational issues are reviewed and summarized to give prospective researchers a clear concept and tip-off on finding efficient solutions for future power system flexibility management. Conclusively, an example problem is simulated for the visualization of the formulation of UC problems with ESSs and solutions.

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Multi objective unit commitment with voltage stability and PV uncertainty

Cited by 60 publications

References 12 publications

Optimal Design of Hybrid PV-Battery System in Residential Buildings: End-User Economics, and PV Penetration

Optimal Design of Hybrid PV-Battery System in Residential Buildings: End-User Economics, and PV Penetration

Optimized Charging of PV-Batteries for Households Using Real-Time Pricing Scheme: A Model and Heuristics-Based Implementation

Energy Storage System Analysis Review for Optimal Unit Commitment

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