• Energy Research

Uncertainties associated with the loads and the output power of distributed generations create challenges in quantifying the integration limits of distributed generations in distribution networks, i.e., hosting capacity. To address this, we propose a distributionally robust optimization-based method to determine the hosting capacity considering the voltage rise, thermal capacity of the feeders and short circuit level constraints. In the proposed method, the uncertain variables are modeled as stochastic variables following ambiguous distributions defined based on the historical data. The distributionally robust optimization model guarantees that the probability of the constraint violation does not exceed a given risk level, which can control robustness of the solution. To solve the distributionally robust optimization model of the hosting capacity, we reformulated it as a joint chance constrained problem, which is solved using the sample average approximation technique. To demonstrate the efficacy of the proposed method, a modified IEEE 33-bus distribution system is used as the test-bed. Simulation results demonstrate how the sample size of historical data affects the hosting capacity. Furthermore, using the proposed method, the impact of electric vehicles aggregated demand and charging stations are investigated on the hosting capacity of different distributed generation technologies.

Uncertainties associated with the loads and the output power of distributed generations create challenges in quantifying the integration limits of distributed generations in distribution networks, i.e., hosting capacity. To address this, we propose a distributionally robust optimization-based method to determine the hosting capacity considering the voltage rise, thermal capacity of the feeders and short circuit level constraints. In the proposed method, the uncertain variables are modeled as stochastic variables following ambiguous distributions defined based on the historical data. The distributionally robust optimization model guarantees that the probability of the constraint violation does not exceed a given risk level, which can control robustness of the solution. To solve the distributionally robust optimization model of the hosting capacity, we reformulated it as a joint chance constrained problem, which is solved using the sample average approximation technique. To demonstrate the efficacy of the proposed method, a modified IEEE 33-bus distribution system is used as the test-bed. Simulation results demonstrate how the sample size of historical data affects the hosting capacity. Furthermore, using the proposed method, the impact of electric vehicles aggregated demand and charging stations are investigated on the hosting capacity of different distributed generation technologies.

Recently, it has been shown that voltage-controlled demand response (VCDR), which is based on the principle of the dependence of the active power of demand on the system voltage, can provide ancillary services to future power systems. Voltage control devices used for VCDR can improve system frequency stability to various extents, depending on their locations, load size and load-voltage dependency in the grid. Thus, designing a supervisory controller to guarantee that such devices are optimally utilized for VCDR is necessary, as this can contribute in enhancing system frequency stability. In this paper, a novel adaptive supervisory controller (ASC) is proposed to optimally use VCDR resources in large power systems for such purposes. To ensure the effective operation of the ASC, an assessment of the impacts of on-load tap changer (OLTC) transformers on the system frequency is essential. In this regard, clustering techniques and principal component regression are used as offline tools to evaluate the influences of OLTCs transformers on VCDR in large-scale power systems using the IEEE 39-bus test system. Also, to estimate the effects of the OLTC transformer clusters on VCDR, a comprehensive sensitivity analysis with respect to the gain of the modified OLTC controllers’ frequency input is conducted.

Recently, it has been shown that voltage-controlled demand response (VCDR), which is based on the principle of the dependence of the active power of demand on the system voltage, can provide ancillary services to future power systems. Voltage control devices used for VCDR can improve system frequency stability to various extents, depending on their locations, load size and load-voltage dependency in the grid. Thus, designing a supervisory controller to guarantee that such devices are optimally utilized for VCDR is necessary, as this can contribute in enhancing system frequency stability. In this paper, a novel adaptive supervisory controller (ASC) is proposed to optimally use VCDR resources in large power systems for such purposes. To ensure the effective operation of the ASC, an assessment of the impacts of on-load tap changer (OLTC) transformers on the system frequency is essential. In this regard, clustering techniques and principal component regression are used as offline tools to evaluate the influences of OLTCs transformers on VCDR in large-scale power systems using the IEEE 39-bus test system. Also, to estimate the effects of the OLTC transformer clusters on VCDR, a comprehensive sensitivity analysis with respect to the gain of the modified OLTC controllers’ frequency input is conducted.

AbstractThis paper proposes a novel method for quantifying fault level in future grid scenarios with various penetrations of power electronics‐connected renewable energy sources. As it is known, the information regarding the fault level is critically important for designing protection schemes, different control loops, understanding voltage profile in the grid, etc. This method is focused on the steady‐state fault level calculation and it can be used to analyse future grid scenarios including uniform and non‐uniform penetration of power electronics‐based generation displacing all, or just specific conventional synchronous generation in the grid. Due to different possibilities for type, size, and location of power electronics‐based RES generation in future grid, it is required to analyse the unprecedented scale of scenarios. The proposed method for FLC enables us to assess the system fault level for large numbers of FG scenarios without a need for detailed system modelling and/or time‐domain simulations. The simulation results demonstrated the suitability of our proposed FLC method for various penetration levels of PE‐based RESs in the 2‐area and the IEEE 39‐bus test systems. The obtained results are compared with time‐domain simulations and the IEC 60909 standards performed in DIgSILENT PowerFactory, where the efficacy of the proposed methodology is demonstrated.

AbstractThis paper proposes a novel method for quantifying fault level in future grid scenarios with various penetrations of power electronics‐connected renewable energy sources. As it is known, the information regarding the fault level is critically important for designing protection schemes, different control loops, understanding voltage profile in the grid, etc. This method is focused on the steady‐state fault level calculation and it can be used to analyse future grid scenarios including uniform and non‐uniform penetration of power electronics‐based generation displacing all, or just specific conventional synchronous generation in the grid. Due to different possibilities for type, size, and location of power electronics‐based RES generation in future grid, it is required to analyse the unprecedented scale of scenarios. The proposed method for FLC enables us to assess the system fault level for large numbers of FG scenarios without a need for detailed system modelling and/or time‐domain simulations. The simulation results demonstrated the suitability of our proposed FLC method for various penetration levels of PE‐based RESs in the 2‐area and the IEEE 39‐bus test systems. The obtained results are compared with time‐domain simulations and the IEC 60909 standards performed in DIgSILENT PowerFactory, where the efficacy of the proposed methodology is demonstrated.

The existing future grid (FG) feasibility studies have mostly considered simple balancing, but largely neglected network related issues and the effect of demand response (DR) for modelling nett future demand. This paper studies the effect of DR on performance of the Australian National Electricity Market in 2020 with the increased penetration of renewable energy sources (RESs). The demand model integrates the aggregated effect of DR in a simplified representation of the effect of market/dispatch processes aiming at minimising the overall cost of supplying electrical energy. The conventional demand model in the optimisation formulation is augmented by including the aggregated effect of numerous price anticipating users equipped with rooftop photovoltaic (PV)-storage systems. Simulation results show that increasing penetration of DR improves loadability and damping of the system with the increased penetration of RESs.

The existing future grid (FG) feasibility studies have mostly considered simple balancing, but largely neglected network related issues and the effect of demand response (DR) for modelling nett future demand. This paper studies the effect of DR on performance of the Australian National Electricity Market in 2020 with the increased penetration of renewable energy sources (RESs). The demand model integrates the aggregated effect of DR in a simplified representation of the effect of market/dispatch processes aiming at minimising the overall cost of supplying electrical energy. The conventional demand model in the optimisation formulation is augmented by including the aggregated effect of numerous price anticipating users equipped with rooftop photovoltaic (PV)-storage systems. Simulation results show that increasing penetration of DR improves loadability and damping of the system with the increased penetration of RESs.