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Electricity demand has grown over the past few years and will continue to grow in the future. The increase in electricity demand is mainly due to industrialization and the shift from a conventional to a smart-grid paradigm. The number of microgrids, renewable energy sources, plug-in electric vehicles and energy storage systems have also risen in recent years. As a result, future electricity grids have to be revamped and adapt to increasing load levels. Thus, new complications associated with future electrical power systems and technologies must be considered. Demand-side management (DSM) programs offer promising solutions to these issues and can considerably improve the reliability and financial performances of electrical power systems. This paper presents a review of various initiatives, techniques, impacts and recent developments of the DSM of electrical power systems. The potential benefits derived by implementing DSM in electrical power networks are presented. An extensive literature survey on the impacts of DSM on the reliability of electrical power systems is also provided for the first time. The research gaps within the broad field of DSM are also identified to provide directions for future work.

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The load shifting strategy is a form of demand side management program suitable for increasing the reliability of power supply in an electrical network. It functions by clipping the load demand that is above an operator-defined level, at which time is known as peak period, and replaces it at off-peak periods. The load shifting strategy is conventionally performed using the preventive load shifting (PLS) program. In this paper, the corrective load shifting (CLS) program is proven as the better alternative. PLS is implemented when power systems experience contingencies that jeopardise the reliability of the power supply, whereas CLS is implemented only when the inadequacy of the power supply is encountered. The disadvantages of the PLS approach are twofold. First, the clipped energy cannot be totally recovered when it is more than the unused capacity of the off-peak period. The unused capacity is the maximum amount of extra load that can be filled before exceeding the operator-defined level. Second, the PLS approach performs load curtailment without discrimination. This means that load clipping is performed as long as the load is above the operator-defined level even if the power supply is adequate. The CLS program has none of these disadvantages because it is implemented only when there is power supply inadequacy, during which the amount of load clipping is mostly much smaller than the unused capacity of the off-peak period. The performance of the CLS was compared with the PLS by considering chronological load model, duty cycle and the probability of start-up failure for peaking and cycling generators, planned maintenance of the generators and load forecast uncertainty. A newly proposed expected-energy-not-recovered (EENR) index and the well-known expected-energy-not-supplied (EENS) were used to evaluate the performance of proposed CLS. Due to the chronological factor and huge combinations of power system states, the sequential Monte Carlo was employed in this study. The results from this paper show that the proposed CLS yields lower EENS and EENR than PLS and is, therefore, a more robust strategy to be implemented.

The penetration of photovoltaic (PV) systems in power grids has substantially increased since the recognition of renewable energies. In a high solar-integrated network environment, an accurate forecast of the expected solar energy output is vital. One of the important factors that influence such forecast is the failure rates of PV systems. Therefore, a new and realistic reliability model of the PV system is proposed in this study. In contrast to the conventional reliability model, which uses fixed values of failure rates in a year, the proposed model considers various weather conditions, detailed PV system architecture, manufacturing quality and other necessary materials to determine the time-varying failure rates of the PV system. Results reveal that the proposed model produces monthly failure rates that are considerably different from the fixed yearly failure rate in which the difference in high latitude regions is more significant than that in tropical climate regions.

Power systems has been subjected to significant upgrades in terms of structure and capacity. Reliability evaluation of composite power systems has surfaced as an essential step in operation and planning stages of the modern power system. It is an effective tool to investigate the ability of power systems to supply customers with reliable power service. The purpose of this review is to enhance the knowledge of reliability studies conducted on composite power systems by providing a critical and systematic review. This work investigates peer-reviewed articles published between 2007 and 2017 in three reliable databases. The findings reveal that the reliability of composite power systems has received considerable attention over the last few years. Secondly, investigation studies demonstrated a crucial role in verifying the impact of adopting new technologies. Third, studies on this topic have been intensively conducted in Asia, which highlights the promising sectors in these regions. However, researchers have generally focused on developing several aspects (e.g., evaluation speed and wind power integration) at the expense of others (e.g., realistic studies and other renewable energy resources). The lack of practical applications is evident in the surveyed publications. These findings imply a potential incoordination between the needs of the real applications and researchers’ tendencies. Future reliability evaluation scholars are advised to consider the findings of this systematic review including concentrating on insufficiently covered topics and enhance the coordination among the efforts devoted in this area.

The dwindling number of conventional power resources and its environmental impact has motivated a transition to renewable energy sources, such as solar power. Evaluating the reliability of solar power integration into power networks can help decision makers gauge the feasibility of their solar power projects. However, the stochastic and non-stationary nature of solar radiation is difficult to be modeled and can even hinder an accurate evaluation of reliability. A good solar model for accurately assessing solar-powerintegrated systems should be able to retain the original statistical properties of the sampled solar radiation data. Therefore, this paper aims to develop a new robust and easy-to-use methodology for simulating solar radiation. The proposed model was compared with four other models, including the clearness index, auto-regressive moving average, and two probability-distribution-based models. Five statistical tests, namely, F-test, diurnal distributions, partial auto-correlation function (PACF), mean, and standard deviation, were performed for the comparison. The comparison results indicate that the proposed method effectively retains the statistical properties of the original data and outperforms all other models in the tests. Therefore, the proposed model can be used for assessing solar-power-integrated power systems.INDEX TERMS Solar radiation model, Monte Carlo, power system reliability analysis, renewable energy.

Integrations of renewable energies, particularly solar and wind, are increasing worldwide due to carbon emission reduction efforts and maturing technologies that have driven down the cost of their energy productions. Due to the intermittency of these renewable sources, the battery energy storage system often coexists alongside solar/wind energy systems. Integrating these two aspects into power systems requires the consideration of reliability, social wellbeing and environmental factors, which collectively form a multi-objective optimization problem that this paper aims to solve with the non-dominated sorting genetic algorithm. The proposed method is able to find optimum solutions that are equally beneficial to all factors -Pareto front -without being heavily biased to any one of them. The proposed method is separated into two parts by first optimizing the penetration of solar/wind energy, followed by the optimization of the energy storage capacity in the second part. The fuzzy decision making method is utilized to select a preferred solution from the Pareto front based on the assignment of the membership function values to reflect operator's preferences. The proposed method was implemented on the IEEE Reliability Test System overlaid with the real sampled weather data. The proposed objectives in the optimization problem are also practical and useful for the expansion of generation systems.