Data Mining - Concepts and Techniques.

In the past, different energy systems were planned and managed independently. But nowadays the development of technologies such as efficient multi-generation system, lead to realizing the benefits of integrated energy infrastructure such as electricity, natural gas, and district heating (DH) networks, and thus a rapid movement toward multi-energy systems (MES). In such systems, different energy carriers and systems interact together in a synergistic way. However, consideration of such a concept requires a suitable tool for integrated management of the system components. Energy hub (EH) that can be defined as the place where the production, conversion, storage and consumption of different energy carriers takes place, is a promising option for integrated management of MES. This paper reviews the different concepts and models used in the literature for EH. The dominant structures used for energy hub models are studied and weaknesses, strengths, and challenges identified and discussed. This article, by identifying these challenges and introducing new options for use in energy hub models, discusses the potentials of energy hub concept, as a comprehensive model of sustainable energy systems in the future.

Energy hub: From a model to a concept – A review

Optimal planning and scheduling of energy hub in presence of wind, storage and demand response under uncertainty

The increase of environmental concerns, scarcity of fossil fuel resources, uncontrolled growth of demand, along with the development of efficient multi-generation systems have made the restructuring of current energy systems inevitable. Future energy systems will be in the form of sustainable multi-energy systems. The optimal operation of such systems requires an integrated energy management system for optimal planning, control and management. Energy hub is a new and promising concept for optimal management of systems with multiple energy carriers. Energy hub has a large potential for realization of energy system models and moving towards sustainable multi-energy systems. This paper provides a comprehensive overview of the concepts and different applications of energy hubs in various energy consumption sectors including residential, commercial, industrial, agricultural, and the integration of these systems. The potential role of energy hub as an integrated energy management system to solve the main challenges in these consumption sectors is evaluated. This study focuses on the benefits earned by integration of the options such as demand side management, distributed energy resources, renewable energy resources, multi-generation systems, storage systems as well as using the smart technologies by introducing the concept of smart energy hubs.

Optimal management of energy hubs and smart energy hubs – A review

In this paper, an mixed integer linear programming (MILP) method is proposed for calculating the optimal power flow in a multicarrier energy system. A state variable-based linear energy hub model is developed, which avoids the introduction of dispatch factor variables applied traditionally to the optimal power flow problem. The multidimensional piecewise linear approximation method is proposed for representing nonconvex natural gas transmission constraints in which the approximation error is further analyzed. Accordingly, the optimal power flow is reformulated as an MILP problem. Compared with the nonlinear models, the proposed model can be solved by the existing optimization techniques, which can be easily implemented in the optimal power system planning problem. The proposed method is verified by case studies applied to the modified six-bus and the IEEE-118 systems. The test results show that the proposed method can provide a fast solution for the optimal power flow which can be applied to large scale hub systems with sufficient accuracy. The results also demonstrate that the proposed method outperforms the existing MILP methods in calculation time especially in large scale hub applications.

An MILP-Based Optimal Power Flow in Multicarrier Energy Systems

Presence of energy hubs in the future vision of energy networks creates a great opportunity for system planners and operators to move towards more efficient systems. The role of energy hubs as the intermediate in multi-carrier energy (MCE) systems calls for a generic framework to study the new upcoming technical as well as economical effects on the system performance. In response, this paper attempts to develop a general optimization and modeling framework for coupled power flow studies on different energy infrastructures. This, as a large-scale nonlinear problem, is approached through a robust optimization technique, i.e., multi-agent genetic algorithm (MAGA). The proposed procedure decomposes the multi-carrier optimal power flow (MCOPF) problem into its traditional separate OPF problem in such a way that the major advantages of simultaneous analysis of MCE systems would not be sacrificed. The presented scheme is then applied to an 11-hubs test system and introduces its expected applicability and robustness in the MCE systems analysis.

A Decomposed Solution to Multiple-Energy Carriers Optimal Power Flow

A majority of remote power systems are going to be supplied by diesel-renewable resources such as wind and photovoltaic energy in the future. However, the unpredictable nature of wind generation increases the concern about the reliable operation of these isolated microgrids. Using energy storage systems (ESSs) is recently accepted as an efficient solution to the volatility and intermittency of renewable energy sources. In this paper, a stochastic programming based on the Monte Carlo approach is introduced for optimal planning of remote systems. So far, most literatures have focused exclusively on the energy storage initial sizing. However, capacity expansion of ESS through the time span can result in significant cost saving and will be illustrated in this paper. Factors such as reliability criteria together with the investment and the operation costs are taken into account in the proposed methodology. This method utilizes practical operational constraints of ESS including efficiency and life cycle. Considering life cycle constraint reinforces the proposed method to completely investigate the difference between ESS technologies. The results of case study demonstrate that the proposed capacity expansion algorithm could lead to about 10% more profit over the traditional energy storage sizing.

Stochastic Capacity Expansion Planning of Remote Microgrids With Wind Farms and Energy Storage

Current-transformer (CT) saturation threatens accurate discrimination between internal fault current and magnetizing inrush current of a transformer and endangers the proper operation of a differential relay. Thus, compensation of CT saturation could substantially improve the performance and security of transformer differential relays. So far, almost all of the technical literature in this field has focused exclusively on fault current compensation. However, this paper proposes an efficient compensation algorithm to reconstruct the fault and inrush currents which are distorted by CT saturation. Some of the attractive features of the proposed method are its accuracy, fast response time, desired sample-by-sample output, no dependency on power system topology, immunity against noise and harmonics, and its simplicity. These features are extracted through the application of numerous test cases reflecting a wide range of variations in fault and inrush currents. The proposed algorithm proves to be fast, accurate, and reliable.

Current-Transformer Saturation Compensation for Transformer Differential Relays

In this paper, a new algorithm is proposed for solving the path planning problem of mobile robots. The algorithm is based on Artificial Potential Field (APF) methods that have been widely used for path planning related problems for more than two decades. While keeping the simplicity of traditional APF methods, our algorithm is built upon new potential functions based on the distances from obstacles, destination point and start point. The algorithm uses the potential field values iteratively to find the optimum points in the workspace in order to form the path from start to destination. The number of iterations depends on the size and shape of the workspace. The performance of the proposed algorithm is tested by conducting simulation experiments.

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Path Planning for Mobile Robots using Iterative Artificial Potential Field Method

Current-transformer (CT) saturation causes severe distortion in the measured current waveform which may lead to maloperation of the protective devices. This paper proposes a low-cost, power-electronic device to prevent the CT from saturation. The proposed compensator is inserted in series with the relay in the CT secondary circuit and acts as a controlled voltage source (CVS). The proposed CVS generates a time-varying voltage to cancel the voltage developed across the CT burden; therefore, the CT magnetic flux remains almost constant and undistorted during the power system transients. It will be shown that this device can precisely compensate fault current, inrush current, and other probable transient currents despite its simplicity. The proposed device can be employed to compensate the already in-service CTs connected to nondigital and digital relays. Comprehensive computer simulations are carried out to validate the effectiveness of the proposed compensator. The performance of a sample compensator of this type is validated through special high-current laboratory experiments (carried out over several hundred amperes up to 1.6 kA), and the obtained results illustrate the capability of the proposed compensator to prevent CT saturation.

Ehsan Hajipour

Papers

A Decomposed Solution to Multiple-Energy Carriers Optimal Power Flow

Stochastic Capacity Expansion Planning of Remote Microgrids With Wind Farms and Energy Storage

Current-Transformer Saturation Compensation for Transformer Differential Relays

Path Planning for Mobile Robots using Iterative Artificial Potential Field Method

Current-Transformer Saturation Prevention Using a Controlled Voltage-Source Compensator