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Changes are emerging that will significantly alter structure and operation of this century's distribution networks, and photovoltaic (PV) systems will play greater role in energy sector, with implications for power system reliability. Considering uncertainties in solar irradiance and electrical loads and incorporating them into the optimization problem within an appropriate methodology is becoming increasingly important in reshaping distribution networks. In this paper, uncertainty scenarios are handled with Monte Carlo Simulation (MCS) under genetic algorithm (GA) and differential evolution- (DE-) based optimization, and probability distribution functions (pdf) of bus voltages and line current are obtained to be used in chance-constrained stochastic programming. This present study focuses on investigating impact of uncertainties in PV system operating under different irradiance scenarios on power loss with probabilistic constraints in distribution networks instead of precise deterministic limits to contribute more efficient and reliable use of energy. By combining meta-heuristic optimization and MCS technique under one framework, this paper contributes to knowledge base of how to allocate PV plants within distribution networks under chance-constrained strategy. In order to show the effectiveness of the proposed methodology, obtained optimization results are tested using MCS under set of uncertainty conditions and network constraints are evaluated for limit violation probabilities. The effectiveness of this method is investigated based on comparative results of two different optimization methods through probabilistic analysis and simulation. [ABSTRACT FROM AUTHOR]

The increased integration of renewable energies (REs) raised the uncertainties of power systems and has changed the approach to dealing with power system challenges. Hence, the uncertain nature of all the power system variables needs to be considered while dealing with the optimal planning and operation of modern power systems. This paper presents a probabilistic optimal active and reactive power dispatch (POARPD) based on the point estimate method (PEM), considering the uncertainties associated with load variation and wind power generation. In the POARPD, the deterministic optimal active and reactive power dispatch (OARPD) is performed in two stages, which gives a deterministic two-stage OARPD (TSOARPD). The objectives of TSOARPD are the operating cost (OC) minimization in stage 1 and voltage stability (VS) maximization in stage 2, whereas the VS is improved by maximizing the system's reactive power reserve (RPR). In this paper, instead of using multiobjective optimal power flow, this TSOARPD is used to give more importance to VS when the system is substantially loaded. The POARPD problem is solved using PEM for modified IEEE-9 bus and standard IEEE-30 bus test systems by considering the correlation between the loads. The results are compared with Monte Carlo simulation (MCS). While solving POARPD, the voltage-dependent load model is used to account for the real-time voltage dependency of power system loads. This paper discusses the detailed procedure of solving POARPD by considering correlation and the increased nonlinearities by giving more importance to VS when the system is heavily loaded. [ABSTRACT FROM AUTHOR]

The paper is proposed an improved equilibrium optimizer (IEO) algorithm to solve the optimal power flow (OPF) problem with the participation of a renewable energy source (RES). In the proposed IEO method, the "exponential term" is replaced by a function that does not dependent on the number of iterations. This modification of the IEO algorithm increases exploration ability compared to EO algorithm. In addition, the exploration of the proposed IEO algorithm will not decrease according to the number of iterations which avoids to get stuck at the local optimal solution. The IEO algorithm is tested on two IEEE 30-bus and IEEE 118-bus systems with three different objective functions. The performance of the proposed IEO method is compared with equilibrium optimizer (EO), artificial ecosystem optimization (AEO), cuckoo search algorithm (CSA), teaching-learning-based optimization (TLBO), artificial bee colony (ABC), and many other existing methods. Besides, a simple probabilistic formula for calculating RES output power based on the Monte-Carlo simulation model is proposed in this paper to reduce the computation time for the OPF problem with RES. The simulation results obtained show that the proposed IEO algorithm has better quality of the solution as well as stability level compared to the original EO algorithm and other algorithm. Thus, the proposed IEO algorithm is also one of effective and reliable algorithms for handling OPF problem with RES. [ABSTRACT FROM AUTHOR]

Due to the strong uncertainties of renewable energy and load demands, the new type of power systems is facing severe challenges in terms of generation control and load dispatch. Considering the uncertainties of the penetrated renewable energy and diversified load demands, this paper proposes a random fuzzy power flow analysis method (RFPF) to exactly depict the impact on the various flows of power systems. In the RFPF, the random fuzzy models of wind turbine generators, photovoltaic generators, and load demands are established utilizing the stochastic probability functions and fuzzy interval to reveal the uncertainties with high precision. Afterwards, the random fuzzy mean value is developed as an index of accuracy, and the fuzzy number of the output variables is extracted under the 95% confidence level. Furthermore, the proposed RFPF model is executed by applying a three-point estimate method (3PE) to figure out the corresponding power flow of the power system, costing less computation burden compared with the Monte Carlo simulation. Simulation studies conducted on the IEEE-33 system verifies the accuracy of the RFPF and the efficiency of the 3PE. [ABSTRACT FROM AUTHOR]

Summary: This paper aims to advance the recently progressing research on solid‐state transformers (SST) and examines the impact of uncertainty on load demand and distributed generation (DG) units while integrating SST into an active distribution network (ADN) in the presence of battery backed solar photovoltaic (BBSPV) and wind generation. The uncertainty related to solar irradiance, wind speeds, and load demands is modeled through kernel density estimation (KDE), generalized extreme value (GEV) distribution, and Gaussian distribution summed with the maximum likelihood estimation (MLE) approach respectively. The K‐medoid clustering procedure is utilized to group the load demand into multiple load levels, which helps in the intelligent scheduling of charging and discharging of battery energy storage systems (BESS). The distribution network planning process manages two conflicting criterion functions of real power loss (RPL) reduction and sum of square deviations of the expected voltage values, which are optimized utilizing the non‐dominated sorting genetic algorithm (NSGA‐II). The probabilistic power flow uses the direct load flow procedure utilizing bus injection to branch current (BIBC) matrix along with the forward sweep method to investigate each run of Monte Carlo simulation (MCS). Multiple case studies have been conducted on the IEEE 33 bus radial distribution network (RDN). The outcomes demonstrate the potential benefits of RPL reduction and voltage profile improvement (VPI) with increasing penetration levels of SST. [ABSTRACT FROM AUTHOR]

Summary: This paper presents an unsupervised clustering algorithm, called modified Iterative Self‐Organizing Data Analysis Technique Algorithm (m‐ISODATA), to capture representative nonchronological scenarios for representing short‐term uncertainties in power system models. The proposed approach is suitable to automatically obtain the number of scenarios required to fully capture the variability of historical series, avoiding the need of adjusting the number of clusters as in techniques commonly used in the literature. The performance of the m‐ISODATA is discussed and compared with Monte Carlo simulation, the well‐known k‐means, and hierarchical agglomerate clustering algorithms. In addition, the obtained scenarios are applied to a wind‐solar‐thermal power system generation expansion planning and to a probabilistic optimal power flow, considering uncertainties over wind and load demand. Finally, the source codes are provided with the best parameters as default. [ABSTRACT FROM AUTHOR]

As wind power penetration becomes increasingly significant, the impact of the intermittent and stochastic wind energy on power systems reliability urgently needs to be assessed. This paper examines the impacts of different wind speed simulation methods on the adequacy evaluation by the conditional reliability indices. First, the seasonal property, diurnal distributions, and autocorrelation of wind speeds at a particular site are investigated with the practical observed wind speed data, and several typical wind speed simulation methods are discussed. Then, after analyzing the drawbacks of the traditional unconditional expected-value-based reliability indices, a series of conditional reliability indices are proposed, making it possible to incorporate risk preference into the reliability assessment. Furthermore, an adequacy evaluation process based on sequential Monte Carlo approach is established. Finally, three wind power integration scenarios are designed in the modified IEEE-RTS, and the impacts of different wind speed simulation methods on the conditional reliability indices in the three scenarios are thoroughly compared. Numerical results have shown that time series methods agree well with the observed wind speed method in the conditional reliability indices; the conditional reliability indices in the probability sampling method present the property of high frequency and short duration, especially in the high damage domain; the mean observed wind speed method leads to relatively optimistic results, with higher conditional probability of the healthy state and lower conditional probabilities of the marginal and at-risk states; and the healthy state in this method is of lower occurrence frequency and longer duration. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]