Synchronized Operation of Grid Power, Solar Power, and Battery for Smart Energy Management

This chapter gives details of solar photovoltaic, starting from its general pros and cons. It covers the basics of site evaluation when installing a solar powered plant and various ways to overcome the uncertainties in the predicted output of the solar arrays. The efficiency of the plant can be improved with the help of maximum power point tracker (MPPT), which works on algorithms based on perturb and observe, incremental conductance, constant voltage, etc. The output of the solar PV arrays can be utilized more effectively by integrating it with grid to supply ac loads. This integration requires a power conditioning system (PCS), enabling smooth operation. Continuity of supply can be maintained by having a battery backup, for the time when both grid and solar array fail to meet the load demand. Such a system can have wide range of applications and has the potential to meet the energy demand.

A Novel Binary Whale Optimization Algorithm-Based Optimal Placement of Phasor Measurement Units

Wide-area measurement system (WAMS) is an important part of present power system structure as it provides real-time synchronized measurements of the system with the aid of phasor measurement units (PMUs). Due to economic considerations, PMUs should be installed at optimal locations. The optimal placement of PMUs (OPP) is a problem of optimally placing PMUs at strategic locations maintaining the full observability of the system. In this chapter, a novel binary whale optimization algorithm (BWOA) is applied to solve OPP problem. The maximization of measurement redundancy is considered in the objective function. The proposed algorithm is examined on five different test systems operating under normal operating conditions with or without inclusion of zero-injection buses (ZIBs) and compared with the reports available in literature. The results show the effectiveness of the proposed algorithm in solving OPPP.

Performance Prediction and Optimization of Solar Water Heater via a Knowledge-Based Machine Learning Method

Measuring the performance of solar energy and heat transfer systems requires a lot of time, economic cost, and manpower. Meanwhile, directly predicting their performance is challenging due to the complicated internal structures. Fortunately, a knowledge-based machine learning method can provide a promising prediction and optimization strategy for the performance of energy systems. In this chapter, the authors show how they utilize the machine learning models trained from a large experimental database to perform precise prediction and optimization on a solar water heater (SWH) system. A new energy system optimization strategy based on a high-throughput screening (HTS) process is proposed. This chapter consists of: 1) comparative studies on varieties of machine learning models (artificial neural networks [ANNs], support vector machine [SVM], and extreme learning machine [ELM]) to predict the performances of SWHs; 2) development of an ANN-based software to assist the quick prediction; and 3) introduction of a computational HTS method to design a high-performance SWH system.

An Optimal Photovoltaic Conversion System for Future Smart Grids

Smart grid technology is the key for a reliable and efficient use of distributed energy resources. Amongst all the renewable sources, solar power takes the prominent position due to its availability in abundance. In this chapter, the authors present smart grid infrastructure issues and integrating solar PV-sourced electricity in the smart grid. Smart grid has many features, including reliability, flexibility on network topology, efficiency, sustainability, and market-enabling. The authors select a photovoltaic active power line conditioner as a case study. This line conditioner is a device designed to extract the maximum power of a photovoltaic (PV) system and to compensate the nonlinear and unbalanced loads of the electrical power systems. The performance of the PV conditioner with the neuro-fuzzy control designed has been analyzed through a simulation platform.

Optimization of Dynamic Power for System on Programmable Chip SOPC

In this chapter, the authors present a new scheduling algorithm that brings a reduction in dynamic power consumption by achieving components scheduling while holding the global latency of the application. The main idea of that algorithm is to augment the latency of some components without impacting the dependency constraint and degrading the global latency of the system. There exist many solutions that manage to increase component's latency; one of them is through decreasing the frequency of their corresponding clocks. Generally, such a method leads to an augmentation in global latency of a system. However, this algorithm manages to reduce the consumed power and hold the same global latency of the system. The presented algorithm has been tested and it provides a significant gain in power at both simulation and physical levels.

Market Power in Deregulated Power System

In today's competitive market, deregulation of power industry is inevitable. The aim of deregulating the power markets is to bring competition into them and thereby make them more economically efficient. In an economically efficient market, no consumer or producer has the ability to impact on prices by itself or by collaborating with any other participant. However, the electricity wholesale market is not a perfect market and the potential for market power exploitation is an issue. Sometimes private companies collaborate with each other to get more profit, driving the prices to a higher level and thus acquiring a market power which is an anti-competitive practice. Thus, market power is the capability of a seller or a group of sellers to profitably maintain the prices above a competitive level and control the total output for a noteworthy period of time.

Demand-Side Management

Growing demands are causing increased pressure on the electrical infrastructure and perpetually escalated energy prices. Utilities around the world have been considering demand-side management in their strategic planning. The costs of constructing and operating a new capacity generation unit are increasing every day as well as transmission, distribution, and land issues for new generation plants, which force the utilities to search for other alternatives. Here, demand-side management has been implemented as it is less expensive to intelligently influence a load than to build a new power plant or install electrical based storage device. In this chapter, the author has discussed energy efficiency and demand response fulfilling the criteria of energy management which usually tries to take influence onto the energy consumption of a number of energy consumers. The explained demand-side management technical objectives are peak clipping, valley filling, load shifting, load building, energy conservation, and flexible load shape.

Typical Design of Synchronous Controller to Minimize Response Time and Power

As power dissipation and time constraint have become vital challenges during the creation of a digital circuit, researchers' and designers' efforts have increased to figure out new ways of preserving power through the study of its sources and its impacts as well as through the decrease of response time to obtain faster treatments. However, it is widely acknowledged that these two parameters are antagonistic in synchronous systems. In fact, current technologies have managed to further decrease the response time to have a faster circuit at the cost of a considerable simultaneous augmentation in its power or vice versa, which leaves no option for designers but to choose from these two important parameters. Hence, the main objective of this chapter is to propose a design method that simultaneously builds a low power design and provides a faster circuit. For the achievement of that purpose, a controller based on a finite state machine (FSM) has been chosen as an example of synchronous system to prove that the new proposed design can optimize both parameters: time and power.

An Optimizer-Tool-Based Improved Metaheuristic Method for Solving Security Optimal Power Flow

In this chapter, an interactive tool using graphic user interface (GUI) environment-based MATLAB is proposed to solve practical optimal power system planning and control. The main particularity of the proposed tool is to assist student and researchers understanding the mechanism search of new metaheuristic methods. The proposed tool allows users to interact dynamically with the program. The users (students or experts) can set parameters related to a specified metaheuristic method to clearly observe the effect of choosing parameters on the solution quality. In this chapter, a new global optimization method named grey wolf optimizer (GWO) and pattern search algorithm (PS) have been successfully applied within the interactive tool to solve the optimal power flow problem. The robustness of the two proposed metaheuristic methods is validated on many standard power system tests. The proposed interactive optimal power flow tool is expected to be a useful support for students and experts specialized in power system planning and control.

Solar PV Powered Standalone Water Pumping Systems

The standalone solar photovoltaic (PV) powered water pumping system could have either two power conditioning units (PCUs) or a single PCU. The system with single PCU is advantageous over the prior because of low device count, smaller in size, low cost, low losses, and higher efficiency. This chapter includes four such single-stage PV water pumping systems. All the four systems are operated using the integrated control technique which assimilates maximum power point tracking (MPPT), pulse width modulation (PWM) technique, and motor control. But the PV systems differ in the inverter configuration used as well as the employed induction motor (conventional star connected and open-end winding induction motor [OEWIM]). The detailed description of mathematical modeling, design and analysis of all the four PV pumping systems along with the simulation results are presented in this chapter.