Summary Energy crisis and the global impetus to “go green” have encouraged the integration of renewable energy resources, plug‐in electric vehicles, and energy storage systems to the grid. The presence of more than one energy source in the grid necessitates the need for an efficient energy management system to guide the flow of energy. Moreover, the variability and volatile nature of renewable energy sources, uncertainties associated with plug‐in electric vehicles, the electricity price, and the time‐varying load bring new challenges to the power engineers to achieve demand‐supply balance for stable operation of the power system. The energy management system can effectively coordinate the energy sharing/trading among all available energy resources, and supply loads economically in all the conditions for the reliable, secure, and efficient operation of the power system. This paper reviews the framework, objectives, architecture, benefits, and challenges of the energy management system with a comprehensive analysis of different stakeholders and participants involved in it. The review paper gives a critical analysis of the distributed energy resources behavior and different programs such as demand response, demand‐side management, and power quality management implemented in the energy management system. Different uncertainty quantification methods are also summarized. This review paper also presents a comparative and critical analysis of the main optimization techniques used to achieve different energy management system objectives while satisfying multiple constraints. Thus, the review offers numerous recommendations for research and development of the cutting‐edge optimized energy management system applicable for homes, buildings, industries, electric vehicles, and the whole community.

No abstract

Power quality (PQ) issue has attained considerable attention in the last decade due to large penetration of power electronics based loads and/or microprocessor based controlled loads. On one hand these devices introduce power quality problem and on other hand these mal-operate due to the induced power quality problems. PQ disturbances/events cover a broad frequency range with significantly different magnitude variations and can be non-stationary, thus, accurate techniques are required to identify and classify these events/disturbances. This paper presents a comprehensive overview of different techniques used for PQ events' classifications. Various artificial intelligent techniques which are used in PQ event classification are also discussed. Major Key issues and challenges in classifying PQ events are critically examined and outlined.

Electric power systems, around the world, are changing in terms of structure, operation, management and ownership due to technical, financial and ideological reasons. Power system keeps on expanding in terms of geographical areas, assets additions, and penetration of new technologies in generation, transmission and distribution. This makes the electric power system complex, heavily stressed and thereby vulnerable to cascade outages. The conventional methods in solving the power system design, planning, operation and control problems have been very extensively used for different applications but these methods suffer from several difficulties due to necessities of derivative existence, providing suboptimal solutions, etc. Computation intelligent (CI) methods can give better solution in several conditions and are being widely applied in the electrical engineering applications. This paper highlights the application of computational intelligence methods in power system problems. Various types of CI methods, which are widely used in power system, are also discussed in the brief.

No abstract

No abstract

Purpose – An electrical power system is expected to deliver undistorted sinusoidal, rated voltage and current continuously to the end-users. The problem of power quality (PQ) occurs when there is (are) deviation(s) in voltage and/or current which cause(s) failure or mal-operation of the customer's equipments. Various methods are suggested to detect and classify single PQ event in a power system, the performance of such methods to classify composite PQ events is limited. The purpose of this paper is the classification of composite PQ events in emerging power systems. Design/methodology/approach – This paper proposes an effective method to classify composite PQ events using Hilbert Huang transform (HHT). The performance of probabilistic neural network (PNN) classifier and support vector machine (SVM) classifier to efficiently classify composite PQ events is compared. Findings – The features extracted from HHT are simple yet effective. SVMs and PNN classifiers are used for PQ classification. It is found that PNN classifier outperforms SVM with the classification accuracy of 100 percent. Originality/value – Different PQ signals used for analysis are generated by simulating a practical distribution system of an Indian academic institution.

No abstract