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This paper proposes a model-based design approach for microgrids considering the aging of power electronic devices under different operating conditions. The proposed approach takes into account the physics of failure mechanisms in converter components as the failure prone devices in power systems. Furthermore, it considers the impacts of load profile on the aging of converter components and system performance. This approach facilitates accurate and optimized design of power electronic-based microgrids. According to this approach fragile links of power converters for a specified application can be identified. Moreover, the weakest converters based on their function in the microgrid can be recognized. As a result, economic decision making within design and planning of microgrids can be achieved.

The use of energy storage systems (ESS) and distributed generators (DGs) to improve reliability is one of the solutions that has received much attention from researchers today. In this study, we utilize a multi-objective optimization method for optimal planning of distributed generators in electric distribution networks from the perspective of multi-objective optimization. The objective is to improve the reliability of the network while reducing the annual cost and network losses. A modified version of the multi-objective sine–cosine algorithm is used to determine the optimal size, location, and type of DGs and the optimal capacity, location, and operation strategy of the ESS. Three case studies of IEEE 33-bus, 69-bus and 141-bus test systems with Turkish DG and load data were conducted to validate the effectiveness of the proposed approach. The distribution of the Pareto front solutions and the optimal objective functions are compared with the other known algorithms. The simulation results show that the average energy not supplied and annual energy losses for the test systems are reduced by up to 68% and 64%, respectively. Moreover, the Pareto fronts of the proposed method show a better distribution and dominate those obtained by MOGWO, MOSMA, NSGA-II, MOPSO and MOEA-D according to three different Pareto optimization metrics. Finally, the computational effort result shows faster convergence of MOSCA compared to MOGWO, MOSMA, NSGA-II, MOPSO and MOEAD.

This article explores the impacts of non-exponentially distributed failures on reliability of microgrids. Failure rate of some components such as power electronic converters is not constant, while they play a major role in microgrids. Consequently, their failure characteristics will affect the microgrid reliability. Hence, the conventional reliability evaluation approaches based on mean time to failure may introduce inaccurate inputs for decision making in planning and operation of microgrids. In this article, different approaches are employed for evaluating the reliability of microgrids with non-constant failure rates. The obtained results indicate that the system reliability remarkably depends on the failure characteristics and considering mean or steady-state probabilities instead of failure statistics may introduce erroneous reliability prediction results. Numerical case studies are provided to illustrate the impacts of failure characteristics on the availability of single power units as well as the reliability of microgrids.

Accurate customer baseline load (CBL) estimation is critical for implementing incentive-based demand response (DR) programs. The increasing penetration of grid-tied distributed photovoltaic systems (DPVS) complicates customers’ load patterns, making the CBL estimation more difficult because the volatile actual load and the intermittent PV output power are coupled together. A PV-load decoupling framework is proposed in this paper to address the above issue. The basic idea is to decouple the actual load power and the PV output power, then estimate them separately. To this end, historical PV output power data of each individual DPVS is required. However, pure historical PV output power data is usually unavailable for small-scale DPVSs, since they are normally located behind the meter, thus only the net load (i.e. actual load power minus PV output power) data is metered. Therefore, this paper proposes a machine learning approach to disaggregate the output power of each individual DPVS from net load data. The proposed approach includes two stages: DPVS capacity estimation and PV output power estimation. The first stage consists of two steps. First, a net load curve optimal pairing-based feature extraction method is proposed to extract features from the discrepancy between two different net load curves of the customers under heterogeneous weather conditions. Second, a multiple support vector regression-based ensemble model with the input features extracted in the first step is established to estimate the DPVS capacity. In the second stage, the output power of each DPVS is estimated by its capacity multiplied by the output power of a standard DPVS. Case studies using a real dataset from Sydney indicate that the proposed approach shows a promising performance on PV output power estimation and can significantly improve the CBL estimation accuracy for customers with DPVSs.

Thermal power generation needs great volumes of water for cooling purposes. A multi-period Low Water Generation Expansion Planning (LW-GEP) model is proposed to plan the future generation mix including type, capacity, time of installation, and proper cooling system of generation technologies to supply the future electric peak loads under the water resource limitations. Different types of generation technologies with conventional and modern cooling systems are considered as expansion candidates in the proposed LW-GEP model. The access of candidate and existing power plants to the water resources are considered. Renewable resources as water smart solutions are integrated in the proposed energy planning model. According to the obtained results, the regional water use constraints impact the generation mix significantly in favor of the dry-cooled and open cycle units. Using the proposed LW-GEP model, the total saving in water consumption for the simulated large scale power system, in regions with water stress and scarcity conditions, reaches to 3.7 × 10 9 m 3 , that is approximately equal to the water consumption of a metropolis such as Tehran in three years. Also, it is shown that the integration of renewable resources as water smart solutions results in reduction of expansion planning cost and more water saving.

This paper develops a mathematical model based on the stochastic programming for simultaneous generation expansion and retirement planning. Retirement decision of the existing generating units is accommodated since these units are aging more and more. The proposed method is formulated as an optimization problem in which the objective function is to minimize the expected total cost consisting of the investment required for commissioning new units, operation and maintenance costs, the retirement salvage cost, and the system risk cost. The problem is subjected to a set of generating unit and system physical and operational constraints. The modeling of energy limited units is also devised in a probabilistic manner as an underlying requirement in practical studies. The Monte Carlo simulation approach is used to consider the component random outages. A large number of scenarios are simulated and the scenario reduction technique is applied to tailor the computational effort within a tractable range, which is essential for large-scale problems. Numerical studies are conducted on the IEEE-RTS79 and the performance of the proposed model is investigated. As expected, the retirement option could be beneficial particularly when the contribution of aged units in the system unreliability becomes more severe.

Confronted with the power system restructuring trend reforming the past-regulated power systems, the need for a narrower insight on the costly maintenance strategies seems imperative. It falls within the realm of reliability centered maintenance to enhance the cost effectiveness of power distribution maintenance policies. From a practical point of view, this paper devises a novel approach on the basis of the analytical hierarchical process (AHP) accompanied by fuzzy sets theory to determine the most critical component types of distribution power systems to be prioritized in maintenance scheduling. In the presence of many qualitative and quantitative attributes, fuzzy sets can effectively help to deal with the existent uncertainty and judgment vagueness. As demonstrated in a practical case study, the proposed fuzzy AHP method introduces its applicability and efficiency in the asset management procedure.

This paper presents a reliability assessment algorithm for distribution systems using a Static Series Voltage Regulator (SSVR). Furthermore, this algorithm considers the effects of Distributed Generation (DG) units, alternative sources, system reconfiguration, load shedding and load adding on distribution system reliability indices. In this algorithm, load points are classified into 8 types and separated restoration times are considered for each class. Comparative studies are conducted to investigate the impacts of DG and alternative source unavailability on the distribution system reliability. For reliability assessment, the customer-oriented reliability indices such as SAIFI, SAIDI, CAIDI ASUI and also load- and energy-oriented indices such as ENS and AENS are evaluated. The effectiveness of the proposed algorithm is examined on the two standard distribution systems consisting of 33 and 69 nodes. The best location of the SSVR in distribution systems is determined based on different reliability indices, separately. Results show that the proposed algorithm is efficient for large-scale radial distribution systems and can accommodate the effects of fault isolation and load restoration.

Despite the significant research efforts devoted to the microgrid and smart grid areas, numerous problems related to real world implementations still remain unsolved. The present special issue was announced with the objective of addressing and disseminating state-of-the-art R&D results on microgrids to bring together researchers from both academia and industry with the goal of fostering interactions among stakeholders. In response, 190 two-page extended abstracts were received and considered for the first round of reviews. Authors of about 60 selected abstracts were then invited to submit the full papers in the second round and out of them 27 high-quality manuscripts were ultimately approved and included in this special issue. These papers are organized according to the following four topics: 1) Microgrid dynamic performance, control/operational strategies, and voltage/frequency regulation. Here, 14 papers deal with the dynamic modeling and performance ofmicrogrid or its building blocks and propose appropriate control/operational strategies or voltage/frequency regulation approaches. 2) Reliability and power quality aspects of microgrids, where seven papers about the microgrid reliability assessment techniques, impact of distributed energy resources on microgrid reliability indices, and the microgrid power quality analysis are presented. 3) Fault modeling and protection schemes. This section consists of four papers focusing on series fault modeling in dc microgrids, overvoltage protection of photovoltaic generators, and protection schemes tailored for the microgrid operational characteristics. 4) Others: Two practical papers about the impact of microgrids on the planning of the medium-voltage distribution networks and characterization of the Vanadium Redox battery are presented.

This paper presents a novel approach for evaluating the reliability of protective systems taking into account its components reliability. In this paper, a previously proposed extended model is used for a directional over-current scheme. In the extended model, the impacts of individual protective components are taken into account. An optimum routine test schedule is determined for each protective component as a separate unit. A comparison is made to show that the proposed approach has excellence over conventional routine test inspections. Impacts of factors such as circuit breaker inadvertent opening, required time for performing routine test inspections, human mistakes and self-checking and monitoring effectiveness is analyzed using the model. Redundancy in some parts of the protective system is examined. Permanent and transient faults on the protected zone, operation of backup protection and common-cause failures are also recognized in the model.

In this paper, some models have been introduced for dynamic thermal modeling of cast-resin transformers which are based on genetic algorithm. A typical 400kVA cast-resin transformer has been selected and the model parameters are estimated and validated with the help of experimental data extracted from different load cycles. The estimated models are used to generate a reliability model for cast-resin dry-type transformers and the effects of variation in load, ambient temperature and atmosphere conditions on the reliability of cast-resin transformer are illustrated. As it will be shown in this paper, the effects of working conditions on the life-time of insulation in dry-type transformers is very considerable and because of highest temperature range (in comparison with oil-immersed transformer), reliability is more sensitive to variation of load, ambient temperature and atmosphere conditions.