Asim Kaygusuz

Dynamic energy pricing by closed-loop fractional-order PI control system and energy balancing in smart grid energy markets

This study investigates dynamic energy price regulation by a closed-loop fractional-order PI control system and presents a possible application for the automated energy balancing in smart grid energy markets. A persistent balance of energy demand and generation is a substantial problem for future smart grids due to the uncertainty and high fluctuation in the generation of distributed renewable energy sources and elastic demand conditions. Dynamic energy pricing is an effective strategy to balance flexible energy markets and it can provide the best energy price that balances energy demand and generation. We numerically demonstrate that closed-loop generation control by using dynamic pricing can provide persistent settling to the best price point of demand and supply curves, when the energy balance error is defined as the difference between instant demand and generation potentials. We analyse energy market management performance of a fractional-order PI controller in the case of communication and operation delays in a multi-source energy market model. Market simulation results are discussed to demonstrate the possible advantages of the fractional-order PI controller for smart grid energy market managements.

Opportunities for energy efficiency in smart cities

Rapid migration from rural to urban areas leads to the emergence of urbanization and sustainability problems. Management and monitoring of resources and infrastructures are getting more important today in crowded cities. Energy consumption is increasing with the growing population and intensified in highly populated parts of cities. This increased energy consumption results in high energy demand as well as the production of more pollutants and heat in these city parts. The management of pollution and heat problems leads to an additional energy demand. Therefore, energy efficiency is becoming central challenge for urban life similar to energy efficient structuring of living organisms. Practically, all activities arranging city life require energy. For instance, job activities, transportation, security, climate, catering, entertainment, commerce etc. In this study, we review active and passive approaches, which can be utilized for improvement of energy efficiency and thus sustainability of urbanization in todays cities. We give a discussion to present a vision for future smart cities in term of improvement of energy utilization and efficiency, and relieve of environmental issues. These approaches can be helpful for future cities for relieving issues originating from population increase.

A note on demand side load management by maximum power limited load shedding algorithm for smart grids

Demand side load management (DSLM) applications improve grid flexibility and support intermittent renewable energy utilization. In this study, we discuss role of power limited load shedding algorithm for DSLM applications in domestic scales. By using this algorithm, domestic power consumption is limited with respect to dynamic electricity price signal and renewable energy generation level by switching off the low prior home electronics appliances (HEAs). The proposed load shedding scheme performs depending on power requirements of HEAs and their user priority presetting. In this study, power limited load shedding algorithm is improved to consider operation modes, which are grid mode, islanded mode and grid+renewable energy (RE) mode and effects of the proposed scheme on domestic consumption is discussed by using simulation results.

Multi-source energy mixing by time rate multiple PWM for microgrids

Smart grid applications aims to employ many optimization and artificial intelligence methods for optimal energy management. Results of these algorithms need to be implemented by power systems in practice. This study presents Time Rate Multiple Pulse Width Modulation (TRM-PWM) methods for multi-source energy mixing for DC microgrids, which is integrating several renewable energy resources. In the paper, we implement TRM-PWM multi-source energy mixer component for integration of energy flow coming from solar and wind energy systems, battery system and grid in MATLAB simulink. Simulation results show that proposed energy mixer component can adjust rate of energy mixing from different sources. By using this component, optimal energy mixing, which can be adjusted by optimization and artificial intelligence methods, can be realized in smart grid applications.

Frequency deviation indicators for estimation of energy balance state in smart AC grids

Due to increasing intermittent renewable energy utilization in smart grids, smart management of energy balance is becoming major concern of research studies. In the case of highly volatile generation and demand conditions, it is very important to estimate instant energy balance state of electricity grid to preserve energy balance automatically and avoid from the cases of generation deficiency and overloading. The utility frequency deviation is commonly used to restore power imbalance in AC grids. This study gives a discussion on the utilization of frequency deviation of AC power system as an indicator of energy balance state in smart grids. We consider two approaches to estimate instant AC frequency deviation of power systems under serious harmonic and noise conditions. The first one is based on frequency to amplitude conversion technique, which use band-pass ramp filtering of AC signal. The other method is based on the sinusoidal to pulse waveform conversion, and it uses measurement of pulse periods of the resulting square waveforms. We compare these two methods and discuss their advantages and disadvantages to generate a balance error signal for smart grid applications. A brief discussion on application of these signals for smart grid energy management is given.

Multi-source energy mixing for renewable energy microgrids by particle swarm optimization

Distributed intelligence is one of the prominent prospects of future smart grids besides distributed generation, distributed storage and demand side load management. This study illustrates utilization of particle swarm optimization (PSO) method for cost-efficient energy management in multi-source renewable energy microgrids. PSO algorithm is used to find out optimal energy mixing rates that can minimize daily energy cost of a renewable microgrids under energy balance and anti-islanding constraints. The optimal energy mixing rates can be used by multi-pulse width modulation (M-PWM) energy mixer units. In our numerical analyses, we consider a multi-source renewable energy grid scenario that includes solar energy system, wind energy system, battery system and utility grid connection. We assume that variable energy pricing is used in utility grid to control energy dispatches between microgrids. This numerical analysis shows that the proposed scheme can adjust energy mixing rates for M-PWM energy mixers to achieve the cost-efficient and energy balanced management of microgrid under varying generation, demand and price conditions. The proposed method illustrates an implementation of distributed intelligence in smart grids.

POWER FLOW STUDY FOR A MICROGRID BY USING MATLAB AND POWERWORLD SIMULATOR

In the near future, the use of energy sources such as solar and wind in power systems will increase. Microgrids are hybrid structures as formed the integration of renewable energy sources with conventional energy generation plants. Microgrids can provide uninterrupted energy for users. The power system must be operated correctly and effectively so that the power flow in the power system is continuous. The paper demonstrates a case study for a power flow analysis. First, the results were calculated and obtained in Matlab software by using the Gauss-Seidel method. Then the system was designed in the PowerWorld simulator and the success of both programs was examined

Load control techniques in smart grids

Population growth and industrialization increase the electrical energy demands. Electrical energy demands from the system should be on the time, reliable and economical. So, large energy consumption with high losses enhances the importance of smart grids. In this paper, facilitating the smart grid as load management techniques are examined. Smart lighting, smart house systems, smart inverters, replacing motors, reactive compensation and harmonic filtering were investigated.

Communication opportunities in smart grids

The studies concentrate on the new generation electric power systems called smart grid for solution of energy management issues. An essential characteristic of the smart grid is the integration of efficient, reliable and secure communication networks to manage the complex power systems. In this paper, the communication architectures in the power systems, including the communication technologies, network requirements, advantages and application challenges are studied. This study summarizes the current state of the communication networks in the smart grid.

Application of reactive power compensation to systems with renewable energy sources

In todays electric power grids, the integration of distributed renewable energy sources gradually becomes popular with the increasing usage of them in the power system. In this study, the load-flow analysis is realized for situations participating the time-based average daily production profiles and modeled distributed renewable sources to IEEE 39 bus system. In these analyzes, the problems are revealed with the analyzing the level of intraday bus voltage, power factor, active power, reactive power and generator reactive power changes. The load-flow is realized with synchronous generator, shunt reactors and shunt capacitor connected to convenient bus of system and the eliminating of the above-mentioned drawbacks is determined.