Real-Time Central Demand Response for Primary Frequency Regulation in Microgrids (original) (raw)
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In order to have a reliable microgrid (MG) system, we need to keep the frequency within an acceptable range. However, due to disturbances in a MG system (such as a sudden load change), it can experience major or minor deviations in frequency, which needs to be reduced within seconds to provide the system stability. In order to maintain the balance between energy supply and demand, traditionally, generation side controllers are utilized to stabilize the power system frequency. These systems add high operational cost, which is not desired for power system operators. With the introduction of smart grid, more and more renewable energy sources are to be used in the power system. The intermittent behavior of these energy resources, as well as high operation cost of conventional controllers, has led to research for new alternatives. In a smart grid environment, demand response (DR) programs can be considered as a promising alternative to the conventional controllers, to efficiently contrib...
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Frequency stability, as one of the most important issues in the modern power grids, requires more efficient control methods due to the increasing complexity of the power system, high penetration of distributed generation sources as well as high electrical energy consumption. The challenges become more critical in the case of islanded microgrids (MGs), due to existing no traditional ancillary services of the upstream electric power network. Thus, the modern power grids, such as MGs, need advanced regulation methods to keep the generation-consumption balancing. Demand response (DR) is the recently introduced control approach which guarantees continuous contribution of controllable loads in the system frequency control. In this paper, a new online droop-based DR, generalized droop control (GDC), is introduced to apply in islanded MGs frequency control. An artificial neural network is used for online tuning of droop coefficients in the presented GDC framework. The proposed control approach changes controllable active and reactive loads, using a set of equations based on satisfying dynamics. To evaluate the effectiveness of the proposed control method, several scenarios are simulated in which changes of the system frequency and voltage are studied. Results show significant damping of power-frequency fluctuation and a desirable performance of the closed-loop system.
Frequency Regulation in Smart Microgrids Based on Load Estimation
Journal of Control, Automation and Electrical Systems, 2018
The desired frequency is maintained in Smart Microgrid (SMG) when the generated power matches the grid load. Variability of wind power and fluctuations of the load are the main obstacles for performance improvement of frequency regulation in SMG. Active Power Control (APC) services provided by wind power generators is one of the main sources for performance improvement in frequency regulation. New coordinated APC architecture, which involves simultaneous speed and pitch control actions delivers desired power to the grid despite significant variations of the wind power. A tool-kit with discretetime input estimation algorithms, which estimate input quantity using output measurements is presented. Unmeasurable load fluctuations are estimated with input estimation method using measurements of grid frequency deviation. Desired power for APC is driven by estimated and a priori known loads. This observer-based control method reduces the risk of overshoots and oscillations in frequency regulation loop compared to PID controllers driven directly by the frequency deviation. The stability of the closed loop frequency control system is proved, and simulation results show that observer-based control architecture provides significant improvement of the frequency regulation in SMG.
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In this study, a stochastic programming model is proposed to optimize the performance of a smart microgrid in a short term to minimize operating costs and emissions with renewable sources. In order to achieve an accurate model, the use of a probability density function to predict the wind speed and solar irradiance is proposed. On the other hand, in order to resolve the power produced from the wind and the solar renewable uncertainty of sources, the use of demand response programs with the participation of residential, commercial and industrial consumers is proposed. In this paper, we recommend the use of incentive-based payments as price offer packages in order to implement demand response programs. Results of the simulation are considered in three different cases for the optimization of operational costs and emissions with/without the involvement of demand response. The multi-objective particle swarm optimization method is utilized to solve this problem. In order to validate the proposed model, it is employed on a sample smart micro-grid, and the obtained numerical results clearly indicate the impact of demand side management on reducing the effect of uncertainty induced by the predicted power generation using wind turbines and solar cells.
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The modelling and simulation of smart meter controlled load blocking scheme to support primary frequency response is investigated. In the proposed scheme, the domestic loads are grouped based on their criticality and essentiality and depending on the drop of frequency each group is blocked. The simulation results were obtained for different losses of generation in the GB power system under