Distribution Systems
F. Jabari; M. Shabanzadeh; M. Zeraati
Abstract
Distribution system state estimation (DSSE) is widely used for real-time monitoring of power grids, where different types of metering devices such as phasor measurement units, smart meters, power quality meters, and etc. are installed. The accuracy of estimated states and the system observability level ...
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Distribution system state estimation (DSSE) is widely used for real-time monitoring of power grids, where different types of metering devices such as phasor measurement units, smart meters, power quality meters, and etc. are installed. The accuracy of estimated states and the system observability level depends on the type, number and location of meters and since there are many nodes and branches in such large networks, a highly redundant measurement infrastructure is practically unattainable due to the limited investment budget. Hence, this paper proposes a novel meter placement algorithm aiming to minimize the distribution system state estimation error and enhance the system observability level considering the limited number of available meters or investment cost. To this end, on one hand, Monte Carlo simulation (MCS) is applied to a weighted least squares (WLS) based DSSE to find the nodal voltage magnitude and angle as the state variables under the uncertainty of measurements. A MCS and WLS-DSSE hybrid iterative nonlinear optimization mesh adaptive direct search (NOMADS) algorithm is proposed to obtain the best locations of the voltage measuring units considering a trade-off between the DSSE performance and the investment cost. The uncertainties associated with the voltage measurements are modeled using random errors with normal probability distribution function. The efficiency and applicability of the proposed method are analyzed by its implementation on a 25-node unbalanced radial distribution system and numerical results demonstrate that this method technically outperforms other heuristic algorithms in the literature which are usually computationally intractable or more demanding in finding the optimal meter places under uncertainties. Compared to other recently developed algorithms, the accuracy of the estimated states as well as the runtime of the proposed algorithm are improved significantly especially under severe measuring errors. Moreover, it is capable to find the minimum number of voltage meters ensuring that the system observability criterion and the expected DSSE accuracy are fulfilled under the uncertain operating conditions.
F. Jabari; M. Zeraati; M. Sheibani; H. Arasteh
Abstract
In the semi-autonomous regions and remote islands, the multiple diesel units are usually used for supplying demand and exchanging power with other adjacent zones. In the risk-aware generation companies consisting of diesel engines, photovoltaic panels (PVs), and wind turbines, the uncertain electricity ...
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In the semi-autonomous regions and remote islands, the multiple diesel units are usually used for supplying demand and exchanging power with other adjacent zones. In the risk-aware generation companies consisting of diesel engines, photovoltaic panels (PVs), and wind turbines, the uncertain electricity market prices affect the optimum operating points of these units, the total revenue gained from selling energy to neighbor microgrids, and the daily fuel cost of the diesel generators. Moreover, the output power of the diesel engines is a nonlinear function of their specific fuel consumption at discrete loading intervals. Therefore, this paper aims to present a risk-aware mixed integer nonlinear optimization problem for finding the best generation schedules of the diesel units involving the energy price fluctuations. The total fuel costs of the diesel engines minus the total revenue achieved from procuring power for nearby regions is minimized as a cost objective function satisfying the lower and upper generation bounds in each loading subinterval, the load-generation balance criterion, and the nominal capacities of generating units. The cubic spline interpolation is used for accurately fitting the fuel-power curves of the diesel generators at successive loading subintervals because of its zero norm of residual in comparison with 5${}^{th}$ degree and quadratic polynomials. A benchmark microgrid with six diesel generators, PVs and wind turbines is robustly scheduled using the budget of uncertainty with no need to probability distribution and membership functions of energy prices. It is revealed that this strategy is practical for each price-taker generation company, which desires the risk-aversion production patterns of the diesel power production units against the energy market price uncertainty in a specific operating horizon.
