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Voltage regulation, frequency restoration, and reactive/active power sharing are the crucial tasks of the microgrid's secondary control, especially in the islanding operating mode. Because sensors and communication links in a microgrid are subject to noise, it is of paramount value to design a noise-resilient secondary voltage and frequency control. This paper proposes a minimum variance control approach for the secondary control of AC microgrids that can effectively perform noise attenuation, voltage/frequency restoration, and reactive/active power sharing. To this end, the nonlinear generalized minimum variance (NGMV) control approach is introduced to the islanded microgrid's secondary control system. The effectiveness of the proposed control approach is verified by simulating two microgrid test systems in MATLAB.

Filtration-based (FB) power/current allocation of battery-supercapacitor (SC) hybrid energy storage systems (HESSs) is the most common approach in DC microgrid (MG) applications. In this approach, a low-pass or a high-pass filter is utilized to decompose the input power/current of HESS into high-frequency and low-frequency components and then assign the high-frequency parts to SC. Moreover, to avoid the state of charge violation (SoC) of SC, this approach requires a rule-based supervisory controller which may result in the discontinuous operation of SC. This paper first provides a small-signal stability analysis to investigate the impact of an FB current allocation system on the dynamic stability of an islanded DC MG in which a grid-forming HESS supplies a constant power load (CPL). Then, it shows that the continuous operation of SC is essential if the grid-forming HESS is loaded by large CPLs. To address this issue, this paper proposes a model predictive control (MPC) strategy that works in tandem with a high-pass filter to perform the current assignment between the battery and SC. This approach automatically restores the SoC of SC after sudden load changes and limits its SoC variation in a predefined range, so that ensure the continuous operation of SC. As amore »result, the proposed FB-MPC method indirectly enables the MG’s proportional-integral (PI) voltage controller to operate with higher gain values leading to better transient response and voltage quality. The performance of the proposed approach is then validated by simulating the system in MATLAB/Simulink.« less

In recent years, the implementation of the demand response (DR) programs in the power system's scheduling and operation is increased. DR is used to improve the consumers' and power providers' economic condition. That said, optimal power flow is a fundamental concept in the power system operation and control. The impact of exploiting DR programs in the power management of the systems is of significant importance. In this paper, the effect of time-based DR programs on the cost of 24-hour operation of a power system is presented. The effect of the time of use and real-time pricing programs with different participation factors are investigated. In addition, the system's operation cost is studied to analyze the DR programs' role in the current power grids. For this aim, the 14-bus IEEE test system is used to properly implement and simulate the proposed approach.

This paper presents an optimization approach based on mixed-integer programming (MIP) to maximize the profit of the Microgrid (MG) while minimizing the risk in profit (RIP) in the presence of demand response program (DRP). RIP is defined as the risk of gaining less profit from the desired profit values. The uncertainties associated with the RESs and loads are modeled using normal, Beta, and Weibull distribution functions. The simulation studies are performed in GAMS and MATLAB for 5 random days of a year. Although DRP increases the total profit of the MG, it can also increase the risk. The simulation results show that RIP is reduced when downside risk constraint (DRC) is considered along with DRP implementation. Considering DRC significantly reduces the percentage of the risk while slightly decreasinz the profit.

Despite the increasing level of renewable power generation in power grids, fossil fuel power plants still have a significant role in producing carbon emissions. The integration of carbon capturing and storing systems to the conventional power plants can significantly reduce the spread of carbon emissions. In this paper, the economic-emission dispatch of combined renewable and coal power plants equipped with carbon capture systems is addressed in a multi-objective optimization framework. The power systems flexibility is enhanced by hydropower plants, pumped hydro storage, and demand response program. The wind generation and load consumption uncertainties are modeled using stochastic programming. The DC power flow model is implemented on a modified IEEE 24-bus test system. Solving the problem resulted in an optimal Pareto frontier, while the fuzzy decision-making method found the best solution. The sensitivity of the objective functions concerning the generation-side is also investigated.

In the recent years, due to the economic and environmental requirements, the use of distributed generations (DGs) has increased. If DGs have the optimal size and are located at the optimal locations, they are capable of enhancing the voltage profile and reducing the power loss. This paper proposes a new approach to obtain the optimal location and size of DGs. To this end, exchange market algorithm (EMA) is offered to find the optimal size and location of DGs subject to minimizing loss, increasing voltage profile, and improving voltage stability in the distribution systems. The effectiveness of the proposed approach is verified on both 33- and 69-bus IEEE standard systems.