Optimal power dispatch in a multisource system using fuzzy logic control

Meje, Kelebogile Confidence; Bokopane, Lindiwe; Kusakana, K.; Siti, Mukwanga W.

doi:10.1016/j.egyr.2020.11.004

Cited by 9 publications

(3 citation statements)

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“…In this respect, relatively new applications for DSM help grid operators to balance intermittent generation of wind and solar units, especially with timing and sizing of energy demand [46]. In many studies, battery systems and active load control have been investigated to optimize load predictability against uncertainties in RES generation and to solve the scheduling problem [47][48][49][50][51][52][53][54][55][56][57][58]. A smart meter and smart DSM system using FL-based algorithms by addressing the uncertainties of RES is implemented and achieved 15% energy savings by providing digital communication [47].…”

Section: Scheduling and Optımızationmentioning

confidence: 99%

“…Studies have also been carried out on FL-based management strategies to operate power system control successfully due to the difficulty of load fluctuation [50][51][52]. [52] have shown in the study that FL not only provides the load, but diesel generator helps to keep battery charged only in the event of an unforeseen power outage. Intermittent and variable energy generation characteristics of RES negatively affect nominal voltage and frequency values of the system.…”

Section: Scheduling and Optımızationmentioning

confidence: 99%

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Demand-Side Energy Management in Smart Buildings: A Case Study

HASANOVA¹,

Neşe²

2021

European Journal of Technic

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Electrical energy is indispensable in our daily life with the developing technology. The most important feature is reliable and sustainable transmission of electrical energy to consumers is to provide the supply-demand balance in real-time. The ever-increasing demand for electrical energy, gradual depletion of traditional resources used to meet demand, and increasing dependence on foreign sources resulted in diverse electricity generation plants. As a result of this diversity, with the increase in the importance of electricity storage systems and awareness of energy-saving, Demand Side Management (DSM) gains great importance in ensuring supply-demand balance. DSM reduces costs by scheduling consumption instead of increasing generation to balance supply and demand. Residences constitute a large part of energy consumption worldwide, so DSM applications in buildings increase efficient usage of energy. Various management strategies can be used to save energy depending on the building type. In this article, an overview of Energy Management System (EMS) strategies to increase energy efficiency is presented. Then a case study is carried out in a residential model in Matlab/Simulink environment. The electrical devices were controlled with a Fuzzy Logic Controller (FLC), taking into account comfort, cost, and Demand Response (DR). In addition, Renewable Energy Resources (RES) to demonstrate their contribution were modelled and integrated into the system. Finally, case studies were conducted, and a comparative analysis of obtained results was carried out.

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Section: Scheduling and Optımızationmentioning

confidence: 99%

Section: Scheduling and Optımızationmentioning

confidence: 99%

Demand-Side Energy Management in Smart Buildings: A Case Study

HASANOVA¹,

Neşe²

2021

European Journal of Technic

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“…Different algorithms such as fuzzy logic [14,15], TLBO [16,17], ANN [18], and other intelligent artificial algorithms have both advantages and disadvantages compared to the random optimization algorithm discussed in this article. Fuzzy logic is capable of handling problems with uncertain or incomplete data and making decisions based on fuzzy rules [19].…”

Section: Introductionmentioning

confidence: 99%

Day-Ahead and Intra-Day Optimal Scheduling Considering Wind Power Forecasting Errors

et al. 2023

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The aim of this paper is to address the challenges regarding the safety and economics of power system operation after the integration of a high proportion of wind power. In response to the limitations of the literature, which often fails to simultaneously consider both aspects, we propose a solution based on a stochastic optimization scheduling model. Firstly, we consider the uncertainty of day-ahead wind power forecasting errors and establish a multi-scenario day-ahead stochastic optimization scheduling model. By balancing the reserve capacity and economic efficiency in the optimization scheduling, we obtain optimized unit combinations that are applicable to various scenarios. Secondly, we account for the auxiliary service constraints of thermal power units participating in deep peak shaving, and develop an intra-day dynamic economic dispatch model. Through the inclusion of thermal power units and energy storage units in the optimization scheduling, the accommodation capacity of wind power is further enhanced. Lastly, in the electricity market environment, increasing wind power capacity can increase the profits of thermal power peak shaving. However, we observe a trend of initially increasing and subsequently decreasing wind power profits as the wind power capacity increases. Considering system flexibility and the curtailed wind power rate, it is advisable to moderately install grid-connected wind power capacity within the power system. In conclusion, our study demonstrates the effectiveness of the proposed scheduling model in managing day-ahead uncertainty and enhancing the accommodation of wind power.

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