Optimal Reactive Power Scheduling Using Cuckoo Search Algorithm

This paper solves an optimal reactive power scheduling problem in the deregulated power system using the evolutionary based Cuckoo Search Algorithm (CSA). Reactive power scheduling is a very important problem in the power system operation, which is a nonlinear and mixed integer programming problem. It optimizes a specific objective function while satisfying all the equality and inequality constraints. In this paper, CSA is used to determine the optimal settings of control variables such as generator voltages, transformer tap positions and the amount of reactive compensation required to optimize the certain objective functions. The CSA algorithm has been developed from the inspiration that the obligate brood parasitism of some Cuckoo species lay their eggs in nests of other host birds which are of other species. The performance of CSA for solving the proposed optimal reactive power scheduling problem is examined on standard Ward Hale 6 bus, IEEE 30 bus, 57 bus, 118 bus and 300 bus test systems. The simulation results show that the proposed approach is more suitable, effective and efficient compared to other optimization techniques presented in the literature

A survey of big data and machine learning

This paper presents a detailed analysis of big data and machine learning (ML) in the electrical power and energy sector. Big data analytics for smart energy operations, applications, impact, measurement and control, and challenges are presented in this paper. Big data and machine learning approaches need to be applied after analyzing the power system problem carefully. Determining the match between the strengths of big data and machine learning for solving the power system problem is of utmost important. They can be of great help to plan and operate the traditional grid/smart grid (SG). The basics of big data and machine learning are described in detailed manner along with their applications in various fields such as electrical power and energy, health care and life sciences, government, telecommunications, web and digital media, retailers, finance, e-commerce and customer service, etc. Finally, the challenges and opportunities of big data and machine learning are presented in this paper

Study on the performance indicators for smart grids: a comprehensive review

This paper presents a detailed review on performance indicators for smart grid (SG) such as voltage stability enhancement, reliability evaluation, vulnerability assessment, Supervisory Control and Data Acquisition (SCADA) and communication systems. Smart grids reliability assessment can be performed by analytically or by simulation. Analytical method utilizes the load point assessment techniques, whereas the simulation technique uses the Monte Carlo simulation (MCS) technique. The reliability index evaluations will consider the presence or absence of energy storage elements using the simulation technologies such as MCS, and the analytical methods such as systems average interruption frequency index (SAIFI), and other load point indices. This paper also presents the difference between SCADA and substation automation, and the fact that substation automation, though it uses the basic concepts of SCADA, is far more advanced in nature

Power system state estimation using teaching learning-based optimization algorithm

The main goal of this paper is to formulate power system state estimation (SE) problem as a constrained nonlinear programming problem with various constraints and boundary limits on the state variables. SE forms the heart of entire real time control of any power system. In real time environment, the state estimator consists of various modules like observability analysis, network topology processing, SE and bad data processing. The SE problem formulated in this work is solved using teaching leaning-based optimization (TLBO) technique. Difference between the proposed TLBO and the conventional optimization algorithms is that TLBO gives global optimum solution for the present problem. To show the suitability of TLBO for solving SE problem, IEEE 14 bus test system has been selected in this work. The results obtained with TLBO are also compared with conventional weighted least square (WLS) technique and evolutionary based particle swarm optimization (PSO) technique

Multi-Objective based Optimal Energy and Reactive Power Dispatch in Deregulated Electricity Markets

This paper presents a day-ahead (DA) multi-objective based joint energy and reactive power dispatch in the deregulated electricity markets. The traditional social welfare in the centralized electricity markets comprises of customers benefit function and the cost function of active power generation. In this paper, the traditional social welfare is modified to incorporate the cost of both active and reactive power generation. Here, the voltage dependent load modeling is used. This paper brings out the unsuitability of traditional single objective functions, e.g., social welfare maximization (SWM), loss minimization (LM) due to the reduction of amount of load served. Therefore, a multi-objective based optimization is required. This paper proposes four objectives, i.e., SWM, load served maximization (LSM), LM and voltage stability enhancement index (VSEI); and these objectives can be combined as per the operating condition. The simulation studies are performed on IEEE 30 bus test system by considering the both traditional constant load modeling and the proposed voltage dependent load modeling

Multi-objective based economic environmental dispatch with stochastic solar-wind-thermal power system

This paper presents an evolutionary based technique for solving the multi-objective based economic environmental dispatch by considering the stochastic behavior of renewable energy resources (RERs). The power system considered in this paper consists of wind and solar photovoltaic (PV) generators along with conventional thermal energy generators. The RERs are environmentally friendlier, but their intermittent nature affects the system operation. Therefore, the system operator should be aware of these operating conditions and schedule the power output from these resources accordingly. In this paper, the proposed EED problem is solved by considering the nonlinear characteristics of thermal generators, such as ramp rate, valve point loading (VPL), and prohibited operating zones (POZs) effects. The stochastic nature of RERs is handled by the probability distribution analysis. The aim of proposed optimization problem is to minimize operating cost and emission levels by satisfying various operational constraints. In this paper, the single objective optimization problems are solved by using particle swarm optimization (PSO) algorithm, and the multi-objective optimization problem is solved by using the multi-objective PSO algorithm. The feasibility of proposed approach is demonstrated on six generator power system

Electrochemical batteries for smart grid applications

This paper presents a comprehensive review of current trends in battery energy storage systems, focusing on electrochemical storage technologies for Smart Grid applications. Some of the batteries that are in focus for improvement include Lithium-ion, metal-air, Sodium-based batteries and flow batteries. A descriptive review of these batteries and their sub-types are explained along with their suitable applications. An overview of different types and classification of storage systems has been presented in this paper. It also presents an extensive review on different electrochemical batteries, such as lead-acid battery, lithium-based, nickel-based batteries and sodium-based and flow batteries for the purpose of using in electric vehicles in future trends. This paper is going to explore each of the available storage techniques out there based on various characteristics including cost, impact, maintenance, advantages, disadvantages, and protection and potentially make a recommendation regarding an optimal storage technique

Short-term optimal hydro-thermal scheduling using clustered adaptive teaching learning based optimization

In this paper, Clustered Adaptive Teaching Learning Based Optimization (CATLBO) algorithm is proposed for determining the optimal hourly schedule of power generation in a hydro-thermal power system. In the proposed approach, a multi-reservoir cascaded hydro-electric system with a non-linear relationship between water discharge rate, net head and power generation is considered. Constraints such as power balance, water balance, reservoir volume limits and operation limits of hydro and thermal plants are considered. The feasibility and effectiveness of the proposed algorithm is demonstrated through a test system, and the results are compared with existing conventional and evolutionary algorithms. Simulation results reveals that the proposed CATLBO algorithm appears to be the best in terms of convergence speed and optimal cost compared with other techniques

Smart cities: Understanding policies, standards, applications and case studies

This paper presents the integration of required basic facilities of living such as healthcare, education, and infrastructure for building the smart cities. The administrations of smart cities should have the smart governance, safety measures with cultural and social stimulus. Four building blocks of smart cities, i.e., people and environment, smart utilities, smart technology and smart administration are described in the present paper. The aim of this paper is to give a clearer perspective of the key decisions with spatial reference that may assume a key part in the plan of a smart city technique. Application of various technologies, for examples big data, artificial intelligence, machine learning, internet of things (IoT), cloud computing, block chain technology to the smart cities are discussed in this paper. Various challenges of smart cities such as information technology (IT) infrastructure, cost, privacy, security, efficiency, fossil fuel dependency and congested commutes with proposed solutions are also presented in this paper

Comparative analysis of electrochemical energy storage technologies for smart grid

This paper presents a comparative analysis of different forms of electrochemical energy storage technologies for use in the smart grid. This paper addresses various energy storage techniques that are used in the renewable energy sources connected to the smart grid. Energy storage technologies will most likely improve the penetrations of renewable energy on the electricity network. Consequently, energy storage systems could be the key to finally replacing the need for fossil fuel with renewable energy. It is hard to evaluate the different types of energy storage techniques between themselves due to the fact that each technology could be used in a different way and are more like compliments. Subsequently, for the purposes of this paper, it is seen that the use of energy storage technologies will increase the supply, and balances out the demand for energy