This paper proposes a frequency security based Energy Management System (EMS) for a hierarchically controlled grid-connected microgrid. The centralized hierarchical EMS is based on precise energy scheduling of droop controlled inverter interfaced Distributed Energy Resources (DERs). A Latin Hypercube Sampling based methodology is applied to model the uncertainties caused by Renewable Energy Sources (RESs), load deviations, and DERs. Simulations are performed on a microgrid in a grid-connected mode. The results verify the effectiveness of considering droop controlled inverter interfaced DERs in preserving the frequency in a hierarchical energy management system. Using this approach, unpredicted changes in the power output of RESs are controlled and microgrid frequency security is satisfied.

Hierarchical droop controlled frequency optimization and energy management of a grid-connected microgrid

In this paper, a coordinated control method for LFC and SMES systems based on a new robust controller is designed. The proposed controller is used to compensate for frequency deviations related to the power system, to prevent excessive power generation in conventional generators during load disturbances, and to reduce power fluctuations from wind power plants. The new robust controller does not require the measurement of all the power system states and it only uses the output feedback. It also has a higher degree of freedom than the conventional robust controllers (conventional output feedback) and thus it helps improve the system control. The proposed control method is highly robust against load and distributed generation resources (wind turbine) disturbances and it is also robust against the uncertainty of the power system parameters. The proposed method is compared under several scenarios with the coordinated control method for LFC and SMES systems based on Moth Swarm Algorithm-optimized PID controller, the LFC system based on Moth Swarm Algorithm-optimized PID controller with SMES, the coordinated control method for LFC and SMES systems based on Robust Model Predictive Control, and the LFC system based on optimized PID controller without SMES and it puts on satisfactory performance. The simulation was performed in MATLAB.

Coordinated Control of LFC and SMES in the Power System Using a New Robust Controller

The paper analyses the impact of uncertainty in power system on congestion management and minimization of operation cost under overloading condition. In the recent development, Demand Response Programs play a vital role in a smart grid environment. It is an economical and flexible attempt towards the maintenance of system security and reliability. But it has a limited capability, as it totally depends on the effective participation of customers from time to time. The consumer often fails to reduce their load due to some external factors. Thus it is necessary to consider the uncertainty of the responsive loads. For this probabilistic load model is developed on the basis of normal distribution function, in accordance with the historical load data available. Further Monte-Carlo simulation is used and samples are extracted which corresponds to the most probable event. To make the condition more realistic, operational condition during a day (24 hours) is considered. In the end, the multi-stage market clearing procedure is followed and analysis is done for different scenarios: Deterministic as well as Probabilistic. Demand Response Program considered is Time of Use.

Demand response program solution to manage congestion in transmission network considering uncertainty of load

A new framework for congestion management with exact modeling of impacting factors

Utilization of wind turbines as economic and green production units, poses new challenges to the power system planners, mainly due to the stochastic nature of the wind, adding a new source of uncertainty to the power system. Different types of distribution and correlation between this random variable and the system load make conventional method inappropriate for modeling such a correlation. In this paper, the correlation between the wind speed and system load is modeled using Copula, a mathematical tool recently used in the field of the applied science. As the effect of the correlation coefficient is the main concern, the copula modeling technique allows simulating various scenarios with different correlations. The conducted simulations in this paper reveal that the wind speed correlation with the load has a significant effect on the system reliability indices, such as Expected Energy Not Served (EENS) and Loss Of Load Probability (LOLP). Moreover, in this paper the effect of the correlation coefficient on the Effective Load Carrying Capability (ELCC) of the wind turbines is analyzed, too. To perform the aforementioned simulations and analyses, the modified RBTS with an additional wind farm is used.

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Effect of Wind Speed and Load Correlation on ELCC of Wind Turbine Generator

Congestion management in electricity markets is traditionally done using deterministic values of power system parameters considering a fixed network configuration. In this paper, a stochastic programming framework is proposed for congestion management considering the power system uncertainties. The uncertainty sources that are modeled in the proposed stochastic framework consist of contingencies of generating units and branches as well as load forecast errors. The Forced Outage Rate of equipment and the normal distribution function to model load forecast errors are employed in the stochastic programming. Using the roulette wheel mechanism and Monte-Carlo analysis, possible scenarios of power system operating states are generated and a probability is assigned to each scenario. Scenario reduction is adopted as a tradeoff between computation time and solution accuracy. After scenario reduction, stochastic congestion management solution is extracted by aggregation of solutions obtained from feasible scenarios. Congestion management using the proposed stochastic framework provides a more realistic solution compared with the deterministic solution by a reasonable uncertainty cost. Results of testing the proposed stochastic congestion management on the 24-bus reliability test system indicate the efficiency of the proposed framework.

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H A Shayanfar

Papers

Stochastic congestion management considering power system uncertainties