• Energy Research

Abstract This paper addresses the stochastic security constrained unit commitment (SSCUC) problem with flexibility resources for managing the uncertainty of wind power generation (WPG). Departing from the traditional flexibility resources such as the thermal units with fast up/down spinning reserves and transmission switching (TS), this paper explores also the use of demand response (DR) and energy storage (ES) systems in an innovative integrated scheme. The proposed scheme utilizes a stochastic optimization framework to coordinate the flexibility resources dealing with the uncertainty of WPGs and equipment failures. The stochastic optimization model is formulated as a mixed-integer linear programming (MIP), and this problem is large and computationally complex even for medium sized systems. Accordingly, we present a novel accelerating decomposition technique aimed at solving this problem and reducing the number of iterations and CPU time. Numerical simulation results on the modified 6-bus system and on large-scale power systems, i.e. IEEE 118 and 300-bus systems, clearly demonstrate the benefits of applying flexibility resources for uncertainty management and the efficacy of the proposed solution strategy for large-scale systems.

Abstract The increasing penetration of Wind Energy Sources (WES) in an ac-network requires more Flexibility Resources (FRs), such as thermal units and transmission topology control by transmission switching (TS) action. The FRs, especially the TS action, can help to accommodate the intermittent and volatiles from WESs. Nevertheless, obstacles still remain and must be dealt before the TS action can be implemented by the real power systems. Indeed, the challenges comprise AC feasibility, the ability to handle large-scale real power systems and computational complexity. This paper investigates these challenges by developing the TS based on a linearized AC network (LAC-based TS) model that includes a linear representation of reactive power and bus voltage magnitudes. The proposed LAC-based TS model can handle high penetration of WES uncertainty in the stochastic security constrained unit commitment (SCUC). Also, the WES is the only source of uncertainty considered in this paper, which is modeled through an appropriate set of scenarios. Accordingly, the proposed model is formulated as a two-stage stochastic programming problem, wherein, the first-stage relates to the day-ahead scheduling, and the second-stage refers to the real-time operating conditions. An iterative algorithm based on the Benders decomposition method is used to solve the problem. The performance of the proposed model is investigated in details using a modified 6-bus and IEEE 118/662-bus test systems.

Abstract This paper proposes a stochastic model for the scheduling of short-term AC security-constrained unit commitment (AC-SCUC) considering reliability and the value of lost load (VOLL). The uncertainty of load and wind power generation, active and reactive power losses, voltage profile of the network’ buses and congestion management for different VOLLs are investigated in this paper. Furthermore, the random outages of generating unit and transmission lines are modeled based on the scenario trees in the Monte Carlo simulation and the reserve requirements of the power system are implicitly scheduled based on the VOLL and by considering corrective actions of the generation units. A computationally efficient two-stage algorithm based on bender's decomposition is proposed to solve the proposed problem. The first stage deals with the base case where all the network components, the units' outputs and on/off status can be determined based on the forecasting load and wind farms' output. The second stage investigates the stochastic part of the problem and runs the possible scenarios in parallel for all the network elements and the available units of the base case. In the case of any violation for a scenario, a bender's cut is added to the first stage which modifies the commitment state and the power units' outputs in order to tackle the violation for that scenario. The method is applied to the IEEE 118/300-bus test system to assess its applicability and capability.

Abstract Although the primary purpose of a static VAr compensator (SVC) is voltage regulation of the bus by injecting reactive power into the system, it can also improve stability and damping of a power system if placed in the right location. In a power system, transient stability is the ability of the system to maintain stability and oscillation damping after a severe disturbance. Actually, the system is stable if the angles of all generators remain stable after a perturbation. This paper proposes a new method to find the best location of SVC in a power system, considering improvement of the first swing stability (FSS) limit and SVC investment cost reduction, simultaneously. For this purpose two indexes are defined, the stability index and cost index. Stability index and cost index are measures to assess the stability of the system and SVC investment cost, respectively. The bus with the highest values of stability index and cost index is the best location of SVC. The effectiveness of the proposed method to find the appropriate location for SVC is tested on the 9-bus IEEE test system and a 29-bus test system. After choosing the best location, SVC is placed there and the optimal value for the maximum capacitive susceptance of SVC is found using particle swarm optimization (PSO) for an agreement between the improvement of FSS limit and reduction of SVC investment cost.

The transformers which connect the different voltage levels in transmission and distribution systems are supplied with on-load tap changing mechanism. The on-load tap changer maintains the voltage amplitude within the pre-defined limits at the so called regulation bus. In a radial feeder, the on-load tap changers are cascaded. Poor coordination between cascaded tap changers causes improper voltage profile and unnecessary operations of tap changers. A fuzzy logic controller is presented in this paper which improves the operation of cascaded tap changers as well as parallel ones. The proposed controller monitors both primary and secondary voltages of the transformer and orders an upward or downward tap changing operation. Because of locally measurement, the communication is not required between two substations which are apart. The controller is also able to set the time delay of tap changing operations based on the measured voltages, which improves the performance of controller and provides a chance to reduce the unwanted changes of taps and also an opportunity to correct the voltage in a reasonable time.

This paper proposes a new topology for hybrid isolated networks employing a wind turbine (WT), proton exchange membrane fuel cell (PEMFC), photovoltaic, ultra-capacitor (UC), and battery energy storage system (BESS). The PEMFC is used in parallel with the UC and BESS; a configuration that improves the performance of the PEMFC in terms of dynamic response and lifetime. The proposed topology of the PEMFC along with a new model of WT reduces the initial costs of the hybrid isolated network. Due to power demand fluctuations in isolated networks, to maintain the balance between power generation and consumption, intelligent and flexible control methods must be applied. The variations of the solar irradiance, of the wind speed, and of the power consumption in hybrid isolated networks render the classic controllers and even fuzzy controllers inefficient. To overcome this problem, we use a novel intelligent fuzzy controller which is optimized by the teaching-learning-based optimization method. In the structure of the new proposed controller, the range and place of membership functions are optimized to reduce network frequency oscillations and improve network frequency stability. The designed control methodology is evaluated in the proposed isolated network and its performance is compared with the fuzzy controller, proportional integral (PI) controller, and optimal PI controller. To demonstrate the effectiveness of the proposed control strategies, the above-mentioned detailed mathematical and dynamic models of the isolated network were integrated using MATLAB/Simulink.

This article introduces an uncertainty-aware cloud-fog-based framework for power management of smart grids using a multiagent-based system. The power management is a social welfare optimization problem. A multiagent-based algorithm is suggested to solve this problem, in which agents are defined as volunteering consumers and dispatchable generators. In the proposed method, every consumer can voluntarily put a price on its power demand at each interval of operation to benefit from the equal opportunity of contributing to the power management process provided for all generation and consumption units. In addition, the uncertainty analysis using a deep learning method is also applied in a distributive way with the local calculation of prediction intervals for sources with stochastic nature in the system, such as loads, small wind turbines (WTs), and rooftop photovoltaics (PVs). Using the predicted ranges of load demand and stochastic generation outputs, a range for power consumption/generation is also provided for each agent called "preparation range" to demonstrate the predicted boundary, where the accepted power consumption/generation of an agent might occur, considering the uncertain sources. Besides, fog computing is deployed as a critical infrastructure for fast calculation and providing local storage for reasonable pricing. Cloud services are also proposed for virtual applications as efficient databases and computation units. The performance of the proposed framework is examined on two smart grid test systems and compared with other well-known methods. The results prove the capability of the proposed method to obtain the optimal outcomes in a short time for any scale of grid.

This paper proposes a fuzzy logic controller for power management of hybrid wind turbine (WT), photovoltaic (PV), fuel cell (FC), and ultra-capacitor (UC) for stand-alone applications. WT and PV are the primary power sources of the system, and an FC is expected to provide long-term energy balance, whereas the UC is employed as buffer storage for the short-term compensation. The improvement in fuzzy logic strategies enhances dynamic response of the FC/UC power system under various load conditions by suitable rule base design for fuzzy controller. The combined use of FC and UC has the potential for better energy efficiency and lifetime increment of FC technology. These rules are designed such that charging and discharging the UC voltage is controlled automatically. The fuzzy controller creates a balance between production and consumption with determination of the exact reference values for WT, PV, and FC. This causes a good frequency controlled in the hybrid generation power system. The fuzzy logic strategies performance under different condition has been verified by using real weather data practical load demand profile. To demonstrate the effectiveness of the proposed fuzzy logic strategies, comparison with improved frequency control method was performed using matlab/simulink by integrating the detailed mathematical and electrical models of the hybrid generation power system.

In today’s world, the development and continuation of life require energy. Supplying this energy demand requires careful and scientific planning of the energy provided by a variety of products, such as oil, gas, coal, electricity, etc. A new study on the operation of energy carriers called Energy Commitment (EC) is proposed. The purpose of the EC is to set a pattern for the use of energy carriers to supply energy demand, considering technical and economic constraints. EC is a constrained optimization problem that can be solved by using optimization methods. This study suggests the Following Optimization Algorithm (FOA) to solve the EC problem to achieve technical and economic benefits. Minimizing energy supply costs for the total study period is considered as an objective function. The FOA simulates social relationships among the community members who try to improve their community by following each other. Simulation is carried out on a 10-unit energy system supplied by various types of energy carriers that includes transportation, agriculture, industrial, residential, commercial, and public sectors. The results show that the optimal energy supply for a grid with 0.15447 Millions of Barrels of Oil Equivalent (MBOE) of energy demand costs 9.0922 millions dollar for a 24-h study period. However, if the energy supply is not optimal, the costs of operating energy carriers will increase and move away from the optimal economic situation. The economic distribution of electrical demand between 10 power plants and the amount of production units per hour of the study period is determined. The EC outputs are presented, which include an appropriate pattern of energy carrier utilization, energy demand supply costs, appropriate combination of units, and power plant production. The behavior and process of achieving the answer in the convergence curve for the implementation of FOA on EC indicates the exploration and exploitation capacity of FOA. Based on the simulated results, EC provides more information than Unit Commitment (UC) and analyzes the network more efficiently and deeply.