Risk-based optimal operation of hybrid power system using multiobjective optimization

ABSTRACT

This paper solves an optimal generation scheduling problem of hybrid power system considering the risk factor due to uncertain/intermittent nature of renewable energy resources (RERs) and electric vehicles (EVs). The hybrid power system considered in this work includes thermal generating units, RERs such as wind and solar photovoltaic (PV) units, battery energy storage systems (BESSs) and electric vehicles (EVs). Here, the two objective functions are formulated, i.e., minimization of operating cost and system risk, to develop an optimum scheduling strategy of hybrid power system. The objective of proposed approach is to minimize operating cost and system risk levels simultaneously. The operating cost minimization objective consists of costs due to thermal generators, wind farms, solar PV units, EVs, BESSs, and adjustment cost due to uncertainties in RERs and EVs. In this work, Conditional Value at Risk (CVaR) is considered as the risk index, and it is used to quantify the risk due to intermittent nature of RERs and EVs. The main contribution of this paper lies in its ability to determine the optimal generation schedules by optimizing operating cost and risk. These two objectives are solved by using a multiobjective-based nondominated sorting genetic algorithm-II (NSGA-II) algorithm, and it is used to develop a Pareto optimal front. A best-compromised solution is obtained by using fuzzy min-max approach. The proposed approach has been implemented on modified IEEE 30 bus and practical Indian 75 bus test systems. The obtained results show the best-compromised solution between operating cost and system risk level, and the suitability of CVaR for the management of risk associated with the uncertainties due to RERs and EVs.     

A review on optimization techniques for sizing of solar-wind hybrid energy systems

Solar and wind are inexhaustible, abundant, environmentally friendly and freely available renewable energy sources. Integration of these two sources has always been a complex optimization problem which requires efficient planning, designing and control strategies. Many researchers have designed cost effective and efficient hybrid solar-wind energy systems by using various available software tools and optimization algorithms. With the advancement in artificial intelligence methods, various new optimization techniques have been developed in the last few decades. This paper presents state of the art optimization methods applied to hybrid renewable based energy systems. A brief introduction of each technique is presented along with papers published in different reputed journals. This article also reviews different power management, control strategies and multi-objective optimization methods used for hybrid wind-solar systems. A case study is presented to demonstrate the efficacy of some of the algorithms.     

A novel framework-based cuckoo search algorithm for sizing and optimization of grid-independent hybrid renewable energy systems

Hybrid renewable energy systems (HRES) turned into an appealing choice for supplying loads in remote areas. The application of smart grid principals in HRES provides a communication between the load and generation from the HRES. Using smart grid in the HRES will optimally utilize the generating resources to reschedule the loads depending on its importance. This paper presents a new proposed design and optimization simulation program for techno-economic sizing of grid-independent hybrid PV/wind/diesel/battery energy system using Cuckoo search (CS) optimization algorithm. Using of CS will help to get the global minimum cost condition and prevent the simulation to be stuck around local minimum. A new proposed simulation program (NPSP) is acquainted using CS to determine the optimum size of each component of the HRES for the lowest cost of generated energy and the lowest value of dummy energy, at highest reliability. A detailed economic methodology to obtain the price of the generated energy has been introduced. Results showed that using CS reduced the time required to obtain the optimal size with higher accuracy than other techniques used iterative techniques, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). Numerous significant outcomes can be extracted from the proposed program that could help scientists and decision makers.     

Thermoeconomic installation limit of PV/WT hybrid energy systems for Off-grid islands

An ideal off-grid island can become 100% energy-sufficient if one installs renewable energy systems such as solar photovoltaic (PV) and wind turbine (WT) systems. However, the intermittent and uncertain nature of the power supply from renewable energy systems hinders a 100% autonomy level (AL) without an infinite energy storage capacity. The thermoeconomic installation limit (TEIL) of a PV/WT hybrid energy system was studied using hourly weather data and the energy demand profile for off-grid islands. An appropriate battery size for the TEIL was also determined. Given the current installation cost of the hybrid energy system and the battery unit, the AL for a PV/WT hybrid energy system at the TEIL is calculated to be approximately 70%. Above the limit, the size of the energy storage unit and, correspondingly, the total annual cost of the PV/WT hybrid energy system increase sharply.     

Improving the dynamic response of frequency and power in a wind integrated power system by optimal design of compensated superconducting magnetic energy storage

The frequency deviation and power fluctuation need to be controlled in a wind-integrated power system (WIPS) for keeping the balance between system power generation and demand, which support the quality and stability of overall power system. The present paper addresses this problem while concerning the integration of intermittent wind power and load disturbance into the WIPS. With this intent, it proposes the compensated superconducting magnetic energy storage (CSMES) system with proportional integral derivative (PID) controller for improving the frequency and power deviation profile. A novel swarm intelligence-based artificial bee colony (ABC) algorithm is used for optimal design of PID-CSMES system. Robustness of the proposed ABC-based PID-CSMES control strategy is tested in WIPS under various disturbance patterns of load and wind power. To demonstrate the improved dynamic response, their simulation results are compared with particle swarm optimization-based PID-CSMES, PID with SMES, and only PID controller technique. The performance indices and transient response characteristics of frequency and power deviation are used to evaluate and compare the accuracy and efficiency of each controller. Stability of various system configurations is analyzed using eigenvalue location. Comparing the results of different controller in WIPS indicates a substantial improvement in the dynamic response of system frequency and power deviations by utilizing the proposed control strategy.     

Design and analysis of artificial bee-colony-based MPPT algorithm for DFIG-based wind energy conversion systems

In this article, the proposed maximum power point tracking (MPPT) method is designed by taking rotor speed as an optimization problem, which is solved by artificial bee colony (ABC) algorithm to generate the maximum power output. The main advantage of this algorithm is that its optimal solution is independent of the initial positions and requirement of lesser number of control parameters, which leads to simple and robust MPPT algorithm than other algorithm. Furthermore, the hill climb search and particle swarm optimization-based MPPT algorithm are also discussed and the results obtained by these are compared to verify the effectiveness of proposed algorithm. Simulations for MPPT control along with doubly fed induction-generator-based wind energy conversion system is carried out in MATLAB/Simulink environment. Three statistical methods are used to evaluate the accuracy of each MPPT algorithm. All results are analyzed and compared under randomly selected wind as well as real wind speed configuration. Comparison of both numerical and simulation results under two different varying wind speed conditions strongly suggest that the proposed ABC-based MPPT algorithm is superior than other two MPPT algorithms.     

Accurate power-sharing,voltage regulation,and SOC regulation for LVDC microgrid with hybrid energy storage system using artificial neural network

ABSTRACT

In this paper, an artificial neural network-based control strategy is proposed for low voltage DC microgrid (LVDC microgrid) with a hybrid energy storage system (HESS) to improve power-sharing between battery and supercapacitor (SC) to suit the demand-generation imbalance, maintain state-of-charge (SOC) within boundaries and thereby to regulate the dc bus voltage. The conventional controller cannot track the SCs current rapidly with the high-frequency component that will place dynamic stress on the battery, further resulting in shorter battery life. The significant advantage is that in the proposed control strategy, redirections of unwaged battery currents to SCs for fast compensations enhance battery life span. The proposed control strategy effectiveness was investigated by simulations, including a comparison of overshoot/undershoot and settling time in dc bus voltage with a conventional control strategy. The results have been experimentally verified by hardware-in-loop (HIL) on a field-programmable gate array (FPGA)-based real-time simulator.     

Optimal sizing and siting of renewable energy resources in distribution systems considering time varying electrical/heating/cooling loads using PSO algorithm

Distributed Generation (DG) sources based on Renewable Energy (RE) can be the fastest growing power resources in distribution systems due to their environmental friendliness and also the limited sources of fossil fuels. In general, the optimal location and size of DG units have profoundly impacted on the system losses in a distribution network. In the present article, the Particle Swarm Optimization (PSO) algorithm is employed to find the optimal location and size of DG units in a distribution system. The optimal location and size of DG units are determined on the basis of a multi-objective strategy as follows: (i) the minimization of network power losses, (ii) the minimization of the total costs of Distributed Energy Resources (DERs), (iii) the improvement of voltage stability, and (iv) the minimization of greenhouse gas emissions. The related distribution system was assumed to be composed of the fuel cells, wind turbines, photovoltaic arrays, and battery storages. The electrical, cooling, and heating loads were also considered in this article. The heating and cooling requirements of the system consist of time varying water heating load, space heating load, and space cooling load. In this study, the waste and fuel cell were used to produce the required heating and cooling loads in the distribution system. In addition, the absorption chiller was used to supply the required space cooling loads. A detailed performance analysis was carried out on 13 bus radial distribution system to demonstrate the effectiveness of the proposed methodology.     

A coordinated control strategy using supercapacitor energy storage and series dynamic resistor for enhancement of fault ride-through of doubly fed induction generator

Power fluctuation and fault-related complication are the two major issues for doubly fed induction generator (DFIG)-based wind energy conversion system (WECS). The occurrence of fault leads to the rotor over current, stator over current, and DC-link overvoltage as well. These uncertainties may damage the rotor circuit, converter circuit and force the disconnection of wind system from the grid. To get rid of these issues, a supercapacitor energy storage element along with a passive series dynamic resistor (SDR) is suggested in this paper. Supercapacitor energy storage system (SCESS) is located across the DC-link, which able to handle the power fluctuation and the SDR is placed in rotor circuit, which will reduce the overcurrent possibility. Simulation is carried for a DFIG-based WECS for three phase to ground fault and two phase to ground fault. During symmetrical fault as well as asymmetrical fault, various operational disorders appeared such as rotor overcurrent, stator overcurrent and DC-Link overvoltage are found to be within their permissible limits. The results reveal the effectiveness of the proposed strategy over the conventional vector control scheme and SCESS as well.     

Sizing and performance analysis of standalone hybrid photovoltaic/battery/hydrogen storage technology power generation systems based on the energy hub concept

In this study, the optimal sizing and performance analysis of a standalone integrated solar power system equipped with different storage scenarios to supply the power demand of a household is presented. One of the main purposes when applying solar energy resource is to face the increasing environmental pollutions resulting from fossil fuel based electricity sector. To this end, and to compare and examine two energy storage technologies (battery and hydrogen storage technology), three storage scenarios including battery only, hydrogen storage technology only and hybrid storage options are evaluated. An optimization framework based on Energy Hub concept is used to determine the optimum sizes of equipment for the lowest net present cost (NPC) while maintaining the system reliability. It was determined that the most cost effective and reliable case is the system with hybrid storage technology. Also, the effects of solar radiation intensity, the abatement potential of CO₂ emissions and converting excess power to hydrogen on the system’s performance and economics, were investigated and a few noticeable findings were obtained.