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Highlights • A new representative contingency constrained unit commitment model is proposed. • The highly nonlinear LOLP formulation is expressed by a series of linear constraints. • A new umbrella contingency identification process is introduced. • The model strikes a good balance between solution accuracy and computation efficiency. Abstract Spinning reserve (SR) is an important resource to deal with the sudden load change, uncertain renewable energy generation, and component failures in power systems. Surplus SR will cause a higher operating cost while insufficient SR will deteriorate the system reliability level. In this paper, the SR is optimized by solving a loss of load probability constrained unit commitment (LCUC) problem. The loss of load probability (LOLP) is an appealing means to enforce the SR requirement in power systems since it can express reliability visually and explicitly. However, till now, the LCUC has not been addressed well in terms of solution accuracy and computation efficiency, due to the highly nonlinear characteristics of the LOLP. In this context, the characteristics of the LOLP are explicitly analyzed. It is found that the nonlinear LOLP constraint can be theoretically expressed as a series of linear constraints and most of the constraints can be relaxed. Then the original LCUC can be equivalently expressed by a new umbrella contingencies constrained unit commitment (UCCUC) model. An umbrella contingency identification process is proposed and the model is solved by the constraint generation technique. The proposed model possesses high computation efficiency with desirable solution accuracy, and thus it can significantly enhance the practicability of the LCUC based SR optimization method in real power systems. The proposed method was validated by case studies with the IEEE-RTS system and several larger systems. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is the all-around trusted technology that associates natural objects to the web for giving straightforwardness and different functionalities and the hybrid power system has characterized as the power grid incorporated with an extensive network. With the change in innovation and developments needs to tackle the energy crises by utilizing hybrid renewable energy resources. The failure of electrical power in remote territories drives associations to investigate elective arrangements, for example, renewable energy power systems. The energy created by hybrid renewable energy sources are dependent on the variation and load demand, such a renewable power system must be equipped for fulfilling the necessities whenever and store the extra power for usage in deficiency situations. An independent renewable energy network to meet the coveted electric load with some sources, little excess power and minimal cost of energy. The essential goal of the design criteria is to limit the entire cost which incorporates initial, operational and support cost. In this work life-cycle cost (LCC), loss of load probability (LOLP) and loss of power supply probability (LPSP) have considered as the genuine factors and a Generous Transformational Optimization Algorithm (GTOA) has projected to pick the greatest possible configuration of a hybrid power framework. Internet of Things (IoT) conveyed in crossover control framework and gave a valuable proposition about assorted advances and norms of a renewable power source, and it additionally gives a review of a few applications and driving variables of a hybrid control framework. Simulation work done with MATLAB software and result helps the efficiency of the proposed technique and confirm that it is 97% efficiency than other ordinary strategies. [ABSTRACT FROM AUTHOR]

There is growing concern regarding generation adequacy within the power system industry. The ever-increasing injection of intermittent renewable resources makes it harder than before to estimate the reliability of modern power systems using traditional approaches. This paper develops a framework for estimating the reliability of modern power systems that have considerable levels of wind power generation. Monte Carlo simulation is applied using a very efficient importance sampling technique based on the cross-entropy method as well as the Copula theory. Tailor-made importance sampling functions for conventional generation, load, and wind power generation drastically reduce the number of samples required to estimate reliability parameters of interest. The methodology enables simulation of multi-area power systems with considerable amount of correlated wind power generation in each of the different areas. Simulation results confirm the efficiency as well as the accuracy of the proposed method and show that it is orders of magnitude faster than crude Monte Carlo simulation. [ABSTRACT FROM AUTHOR]

• A multi-state reliability model is considered for every physical component to address the interdependency with cyber elements. So, we can represent physical components by independent multi-state models. • The reliability indices were calculated using the significant fewer number of most probable configurations with high accuracy (k = 10 7 configurations out of 20.639 × 10 9 possible states for RBTS and k = 5 × 10 8 out of 1.594 × 10 39 possible states for IEEE-24 system). • The impact of the protection system malfunctions on system was 26.03% for RBTS network and 36.11% for IEEE-24 busses network. • The impact of the protection system malfunctions on substations connected to more transmission lines are higher compared to other substations system (364% and 176% in buses 4 and 3 in RBTS test system and 128% in bus number 9 in IEEE-24). • The most probable failure configuration occurred due to the failure of physical systems and successful operation of cyber elements. Also, the most severe configurations are occurred due to the failure of the process buses (PB) element of the protection system. The reliability indices of composite power systems are vital to supplier companies and planners. With the rapid utilization of information and communication technologies (ICTs), smart power networks include both cyber and physical components. Considering the effects of the cyber elements is unavoidable for the accurate evaluation of system reliability. This paper aims to assess the reliability indices of the composite power systems at the substation level using the most probable configurations of the network. To cover the interdependency between the cyber and physical component's state, all possible events due to the failure of cyber elements are evaluated and a multi-state model instead of a two-state model is computed for each physical component. An algorithm is applied to select the most probable configurations of the system. In addition, the effect of utilizing the dynamic thermal rating (DTR) system, in which the rating of the system components can be improved safely, is considered in the proposed model. Finally, loss of load probability (LOLP), and expected energy not supply (EENS) as the reliability indices are calculated after evaluating the saved failure configurations applying an optimal DC load flow. The proposed approach is applied to the Roy Billinton Test System (RBTS) and the IEEE-24 buses as the small and large test networks respectively. The results show the efficiency of the proposed approach in determining the most probable system configurations and computing the reliability indices with high accuracy. The achieved results reveal that the most probable failure configuration of the system is the state in which the failure occurred in physical components and the cyber elements operate successfully. However, the most severe configuration occurred due to the unsuccessful operation of process buses (PBs) in substations. Furthermore, the reliability indices are improved significantly considering the DTR system instead of the static thermal rating (STR) system. [ABSTRACT FROM AUTHOR]

An Extension Taguchi Method (ETM) is proposed on the optimized allocation of equipment capacity for solar cell power generation, wind power generation, full cells, electrolyzer and hydrogen tanks. The ETM is based on the domain knowledge containing the product specifications and allocation levels provided by suppliers and design factors since most of the renewable energy equipment available in the market comes with a specific capacity. A proper orthogonal array is used to collect 18 sets of simulation responses. The extension theory is introduced to determine the correlation function, and factor effects are used to identify the optimized capacity allocation. The hours of power shortage are simulated using Matlab for all capacity allocations at the lowest establishment cost and the optimized capacity allocation of loss of load probability (LOLP). Finally, the extension theory, extension AHP theory, ETM and Analytic Hierarchy Process (AHP) are used to determine the optimized capacity allocation of the system. Results are compared for the above four optimization simulation methods and verify that the proposed ETM surpasses the others on achieving the optimized capacity allocation. [ABSTRACT FROM AUTHOR]

By intruding on the substations and control center of the supervisory control and data acquisition system, trip commands can be sent to intelligent electronic devices that control the power system breakers. Reliability of the power system can be impacted through the cyberattacks. In this paper, a modified semi-Markov process (SMP) model is used to describe the procedures of normal and penetration attacks against the intrusion tolerant system. By modeling the transition probabilities between the SMP states and sojourn time of each SMP state, the mean times to compromise the normal and penetration attacks are calculated. With increased probabilities of breaker trips resulted from the cyberattacks, the loss of load probabilities are evaluated based on IEEE reliability test system 79. When the level of attack increases or the level of defense in the system decreases, the simulation results demonstrate that the power system becomes less reliable. [ABSTRACT FROM PUBLISHER]

Abstract: The electricity sector in OECD countries is on the brink of a large shift towards low-carbon electricity generation. Power systems after 2030 may consist largely of two low-carbon generator types: Intermittent Renewable Energy Sources (IRES) such as wind and solar PV and thermal generators such as power plants with carbon capture. Combining these two types could lead to conflicts, because IRES require more flexibility from the power system, whereas thermal generators may be relatively inflexible. In this study, we quantify the impacts of large-scale IRES on the power system and its thermal generators, and we discuss how to accurately model IRES impacts on a low-carbon power system. Wind integration studies show that the impacts of wind power on present-day power systems are sizable at penetration rates of around 20% of annual power generation: the combined reserve size increases by 8.6% (6.3–10.8%) of installed wind capacity, and wind power provides 16% (5–27%) of its capacity as firm capacity. Thermal generators are affected by a reduction in their efficiency of 4% (0–9%), and displacement of (mainly natural gas-fired) generators with the highest marginal costs. Of these impacts, only the increase in reserves incurs direct costs of 1–6€/MWhwind. These results are also indicative of the impacts of solar PV and wave power. A comprehensive power system model will be required to model the impacts of IRES in a low-carbon power system, which accounts for: a time step of <1h, detailed IRES production patterns, flexibility constraints of thermal generators and interconnection capacity. Ideally, an efficient reserve sizing methodology and novel flexibility technologies (i.e., high capacity interconnectors and electricity storage and DSM) will also be included. [Copyright &y& Elsevier]

Abstract: This paper presents the design aspects and probabilistic approach for the generation reliability evaluation of an alternative resource: municipal waste water (MWW) based micro-hydro power plant (MHPP). Annual and daily flow duration curves have been obtained for design, installation, development, scientific analysis and reliability evaluation of the MHPP. The hydro-potential of the waste water flowing through sewage system of Brocha sewage plant of the Banaras Hindu University campus is determined to produce annual flow duration and daily flow duration curves by ordering the recorded water flows from maximum to minimum values. Design pressure, roughness of the pipe's interior surface, method of joining, weight, ease of installation, accessibility to the sewage system, design life, maintenance, weather conditions, availability of material, related cost and likelihood of structural damage have been considered for the design of a particular penstock for reliable operation of the developed MHPP. MWW and self-excited induction generator (SEIG) based MHPP is developed and practically implemented to provide reliable electric power to charge the battery bank. Generation reliability evaluation of the developed MHPP using the Gaussian distribution approach, safety factor concept, peak load consideration and Simpson 1/3rd rule has been presented in this paper. [Copyright &y& Elsevier]

Abstract: This paper presents a comparison of results for optimization of captive power plant maintenance scheduling using genetic algorithm (GA) as well as hybrid GA/simulated annealing (SA) techniques. As utilities catered by captive power plants are very sensitive to power failure, therefore both deterministic and stochastic reliability objective functions have been considered to incorporate statutory safety regulations for maintenance of boilers, turbines and generators. The significant contribution of this paper is to incorporate stochastic feature of generating units and that of load using levelized risk method. Another significant contribution of this paper is to evaluate confidence interval for loss of load probability (LOLP) because some variations from optimum schedule are anticipated while executing maintenance schedules due to different real-life unforeseen exigencies. Such exigencies are incorporated in terms of near-optimum schedules obtained from hybrid GA/SA technique during the final stages of convergence. Case studies corroborate that same optimum schedules are obtained using GA and hybrid GA/SA for respective deterministic and stochastic formulations. The comparison of results in terms of interval of confidence for LOLP indicates that levelized risk method adequately incorporates the stochastic nature of power system as compared with levelized reserve method. Also the interval of confidence for LOLP denotes the possible risk in a quantified manner and it is of immense use from perspective of captive power plants intended for quality power. [Copyright &y& Elsevier]

The objective of this paper is to optimize maintenance scheduling of generating units of a captive thermal plant using intelligent optimization techniques such as genetic algorithm (GA) and simulated annealing (SA). A multi-class maintenance scheduling has been considered to incorporate statutory safety regulations for maintenance of boilers, turbines and generators. Such multi-class maintenance scheduling in terms of mathematical formulations necessitates a planning horizon of five years compared to the conventional one to two year horizon. As the utilities catered by captive power plants are very sensitive to power failure, the reliability of power supply has been evaluated by loss of load probability (LOLP) index. The significant contribution of this paper is to evaluate confidence intervals of LOLP instead of conventional point estimate of LOLP. A point-estimate of LOLP uses expected values and provides no information about the range of values over which LOLP may vary. Some variations from optimum schedules are anticipated while executing maintenance schedules due to different real-life unforeseen exigencies. Such exigencies are considered in terms of near-optimum schedules obtained from SA during the final stages of convergence. LOLP evaluated from such sub-optimum schedules forms the basis to treat LOLP as a random variable for subsequent evaluation of confidence intervals. Case studies pertaining to a captive power plant for an aluminium smelter corroborate that the schedules obtained from two optimization techniques yield the same optimum schedule and thus the efficacy of the proposed algorithm is validated. Also the interval of confidence for LOLP denotes the possible risk in a quantified manner and it is of immense use from perspective of captive power plants intended for quality power. [ABSTRACT FROM AUTHOR]

The most robust methods to determine the capacity contribution from intermittent sources combine load curve, variable generation profile, and dispatchable generators' data to calculate any new inserted variable source's capacity value in the power system. However, these methods invariably adopt the premise that the system's dispatchable generators' capacity is constant. That is an unacceptable limitation when the energy mix has a large share of hydroelectric sources. Hydroelectric plants are dispatchable sources with variable maximum power output over time, varying mainly according to the reservoirs' level. This article develops a method that makes it possible to calculate the capacity value from renewable resources when the dispatchable generation units of an electric system have variable capacity. The authors apply the method to calculate the capacity value from solar and wind sources in Brazil as an exercise. By abandoning the hypothesis of constant dispatchable capacity, the developed approach is in principle extensible for other energy-limited resources, such as batteries and concentrating solar power (CSP). This can be a strategy to incorporate energy-limited capacity sources into the planning and operation models as reliable capacity sources. [ABSTRACT FROM AUTHOR]