Reactive Power Loss Index for Identification of Weak Nodes and Reactive Compensation Analysis to Improve Steady State Voltage Stability

This chapter presents a new reactive power loss index for identification of weak buses in the system. This index can be used for identification of weak buses in the systems. The new reactive power loss index is illustrated on sample 5-bus system, and tested on sample 10-bus equivalent system and 72-bus equivalent system of Indian southern region power grid. The validation of the weak buses identification from the reactive power loss index with that from other existing methods in the literature is carried out to demonstrate the effectiveness of the index. Simulation results show that the identification of weak buses in the system from the new reactive power loss index is completely non-iterative, and thus requires minimal computational efforts as compared with other existing methods in the literature.

Voltage Stability Assessment Techniques for Modern Power Systems

In recent times, most of the power systems are made to operate close to their operating limits owing to various reasons like slow pace of transmission line expansion, environmental constraints, deregulated electricity market, etc. Therefore, the issue of maintaining the system stability has become the primary objective of the utility companies. The recent development and integration of renewable energy sources have further pushed the modern power systems to system security risks. The voltage instability had been the major cause of recent blackouts around the world. The timely assessment of voltage stability can prevent the blackouts in the power systems. This chapter explores the classical as well as newly developed static voltage stability assessment techniques proposed by various researchers in recent years. Also, the chapter cater to the needs of undergraduate as well as graduate students, professional engineers, and researchers who all are working in the domain of power system voltage stability.

Electric Vehicle Infrastructure Planning

Widespread adoption of electric vehicles would bring a paradigm shift in the way distribution infrastructure is planned and electricity markets operate. Electric vehicle adoption could help in meeting the worldwide targets for greenhouse gas emissions. Moreover, the health benefits for the public would be immense as the source of emissions would be far away from the massively populated areas. For electricity markets, electric vehicles can serve as a distributed plug in facility of energy storage at low cost requiring minimal capital investment from grid utilities. However, widespread electric vehicle adoption faces a number of hurdles such as limited range in comparison to Internal combustion engines, but from the grid perspective, it faces issues such as limitations of available charging infrastructure to charge large number of electric vehicles and longer charging time currently as compared to refueling fuel driven vehicles. This chapter explores such issues and their remedies in the current literature.

Power System Voltage Stability

Understanding the voltage instability phenomenon and its effects in detail facilitates the research community to develop methodologies that can detect instability in a timely manner. Traditionally voltage instability in the system is identified through P-V and Q-V curves that are plotted using repetitive runs of load flow programs. It is observed that voltage stability is affected by the load dynamics, voltage control devices like OLTC, and hitting of over excitation limiters of the synchronous generators. In the following sections of this chapter, the concept of voltage instability with P-V and Q-V curves, load restoration mechanism with on load tap changer (OLTC), and with different types of loads are briefly presented.

Energy Management

Energy is described as the amount of work that can be done by force. There are various forms of energy such as kinetic energy, potential energy, thermal energy, light energy, sound energy, and electromagnetic energy. As per the law of conservation of energy, it is neither created nor destroyed. In this modern era, energy became an integral part of our life. The life without energy is not at all possible nowadays. The energy is not offered at free of cost and it comes at an affordable prize. The generation of energy requires natural resources which are exhaust day by day. At the same time, the usage of energy is increasing exponentially. Managing and reducing energy consumption not only saves money but also helps in mitigating climate change and enhancing corporate reputation. The organizations can achieve appreciable energy reduction by adopting simple measures. This chapter discuss about the present scenario of energy, need for energy management, energy management program, and its various steps involved.

Probabilistic Power System Reliability Assessment

This chapter presents the different dynamics in power system reliability as a result of the intrinsic behavior of distributed renewable energy sources. The output power of distributed renewable energy sources depends on the amount of available respective resource at any given time. This output power generally experiences fluctuations when compared with the output of conventional power generation units. The phenomenon is not usually included in traditional reliability worth evaluation methods for power system networks with distributed generation. In this chapter, a reliability worth evaluation model for power system networks with time-dependent distributed renewable generation resources is presented and analyzed. Time sequential Monte Carlo simulation technique is used, and the operational efficiency of the distributed generation unit is measured using the primary reliability worth index, ECOST. The derived index is fitted to a beta distribution function to show the inherent skewness of the supply reliability worth index.

Application of Moth-Flame Optimization Algorithm for the Determination of Maximum Loading Limit of Power System

Moth-flame optimization algorithm (MFOA) based on the navigation strategy of moths in universe is a novel bio-inspired optimization technique and has been exerted for determining the maximum loading limit of power system. This process is highly effective for traversing long distances following a straight path. As a matter of fact, moths follow a deadly spiral path as artificial lights tend to confuse them. Exploration and exploitation are two vital aspects of the algorithm, used in tuning of the parameters. The algorithm is verified on MATPOWER case30 and case118 systems. Comparison of the performance of MFOA has been done with other evolutionary algorithms such as multi-agent hybrid PSO (MAHPSO), differential evolution (DA), hybridized DE, and PSO (DEPSO). The performance of MFOA in determining maximum loading limit is verified from the results. In much reduced time, MFO algorithm also gives high maximum loading point (MLP).

Optimized Hybrid Power System Using Superconducting Magnetic Energy Storage System

Renewable energy sources always drag the attention of researchers as alternate sources of power generation. These sources are inexhaustible and free of cost, which makes them very important for fulfilling electrical load demand. Due to stochastic nature of these sources as these are nature dependent, power generation from these sources varies. In order to mitigate this issue, these sources are integrated with distributed generation along with energy storage system so as to maintain the system stability. This chapter focuses on diminishing the frequency variation of microgrid incorporated hybrid power system. A hybrid system consisting of solar, wind, diesel along with a controller and superconducting magnetic energy storage unit is simulated. Whenever load demand of the system increases, frequency falls as a result deviation occurred in the system. This is overcome by the automatic generation control mechanism. Superconducting magnetic energy storage unit absorbs the excessive power available during offload condition and injects the same during peak load condition.

Load Management Using Swarm Intelligence

This chapter presents a generalized day-ahead combined dynamic economic emission dispatch (DEED) problem incorporating demand response (DR) strategy for power system networks with mutual communication between electricity customers and power utility. A nonconvex mixed binary integer programming technique is used to solve the demand response optimization problem. Fixed and flexible home appliances connected as load to the power system network are considered in the demand response strategy. The optimization of the DEED problem is done using particle swarm optimization (PSO) technique. The proposed PSO algorithm takes into account thermal power generation unit ramp rates and their power generation constraints.

An Energy Storage System

The increasing greenhouse emissions have led us to take advantage of renewable sources. The intermittency of these sources can be mitigated using energy storage systems. The present work shows three different strategies depending on the power management and other technical factors, such as energy quality, each one with a specific goal. The first strategy tries to improve the electricity quality, the second tries to reduce the penalties imposed by the grid manager to the power plant, and the third one tries to improve significantly the final economic profit of the generation companies. To achieve the above strategies, an intelligent model approach is explained with the aim to predict the energy demand and generation. These two factors play a key role in all cases. In order to validate the three proposed strategies, the data from a real storage/generation system consisting on an electrolyzer, a hydrogen tank, and a fuel cell were analyzed. In general terms, the three methods were checked, obtaining satisfactory results with an acceptable performance of the created system.