Multi-criteria PSO-based optimal design of grid-connected hybrid renewable energy systems

ABSTRACT

Human-induced climate change through the over liberation of greenhouse gases, resulting in devastating consequences to the environment, is a concern of considerable global significance which has fuelled the diversification to alternative renewable energy sources. The unpredictable nature of renewable resources is an impediment to developing renewable projects. More reliable, effective, and economically feasible renewable energy systems can be established by consolidating various renewable energy sources such as wind and solar into a hybrid system using batteries or back-up units like conventional energy generators or grids. The precise design of these systems is a critical step toward their effective deployment. An optimal sizing strategy was developed based on a heuristic particle swarm optimization (PSO) technique to determine the optimum number and configuration of PV panels, wind turbines, and battery units by minimizing the total system life-cycle cost while maximizing the reliability of the hybrid renewable energy system (HRES) in matching the electricity supply and demand. In addition, by constraining the amount of conventional electricity purchased from the grid, environmental concerns were also considered in the presented method. Various systems with different reliabilities and potential of reducing consumer’s CO₂ emissions were designed and the behavior of the proposed method was comprehensively investigated. An HRES may reduce the annualized cost of energy and carbon footprint significantly.     

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.     

Triple-objective optimal sizing based on dynamic strategy for an islanded hybrid energy microgrid

A triple-objective optimal sizing method based on a dynamic strategy is presented for an islanded hybrid energy microgrid, consisting of wind turbine, solar photovoltaic, battery energy storage system and diesel generator. The dynamic strategy is given based on a dynamic complementary coordination between two different master-slave control modes for maximum renewable energy utilization. Combined with the proposed strategy, NSGA-II-based optimization program is applied to the sizing optimization problem with triple different objectives including the minimization of annualized system cost, the minimization of loss of power supply probability and the maximization of utilization ratio of renewable energy generation. The sizing results and the proposed strategy are both compared and analyzed to validate the proposed method in a real case of an islanded hybrid energy microgrid on Dong’ao Island, China.     

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.     

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.     

Comparative study of photovoltaic technologies based on performance,cost and space requirement: Strategy for selection and application

The module performance is an important consideration for selecting PV technologies for electricity production, as well as the economic aspect. Also, PV energy yield under varying environmental conditions is largely dependent on the type of technology used. Therefore, this article presents a comparative analysis of different PV modules, of the same power, namely, monocrystalline, polycrystalline, amorphous silicon and hybrid, based on performance, cost and space requirement. The performance is evaluated in terms of module power output, yield, capture losses, fill factor and efficiency, according to the IEC 61724 standards, using Gwako, Nigeria as a case study. A novel technique called Fundamental PV Module Performance Analysis is used to analyze and compare the performance of the PV modules. The performance of a single module is then employed to calculate the overall performance of a PV array designed for a small off-grid house, and a suitable module is determined amongst the modules under study. Results provide insights into the behaviors of the different technologies with the environmental factors of the location, which have an impact on their power and kWh/kW outputs and the efficiency. This knowledge, coupled with the understanding of the constraints of cost and the module space requirements would be useful to researchers, engineers, installers etc. in Nigeria, for planning and developing photovoltaic electric systems for off-grid applications.     

Techno-economic and environmental analysis of an integrated standalone hybrid solar hydrogen system to supply CCHP loads of a greenhouse in Iran

The techno-economic and environmental performance of hybrid solar hydrogen energy systems was investigated to provide combined cooling, heating and power (CCHP) demands of a standalone greenhouse in Iran to achieve sustainable agriculture based on an optimization procedure. From the environmental point of view, by deploying hybrid energy systems, 83%, to 100% of emissions can be avoided. Also a sensitivity analysis was performed on the hybrid energy systems in order to study the effect of major parameter variation on the systems justification. It was concluded that hybrid solar systems are economically competitive with conventional systems, for high solar intensity locations with high diesel fuel prices and decreased prices for PV and hydrogen storage technology.     

Optimization of grid tied hybrid power system in smart premises

Tapping of renewable energy sources like solar and wind is given great priority by power producers all over the world. Technical problems of linking them to the grid are solved. The cost constraints of utilizing renewable energy at specific locations are to be determined. In this work, a model is developed for grid tied hybrid power system (HPS) consisting of photovoltaic (PV) module and wind mill at the roof top of smart premises. The grid is capable of delivering and receiving energy. Objective function is formed with constraints taking into account the cost of PV module, wind mill, and grid tied inverter with controller. The constraints are rating of HPS and energy that can be delivered to the grid. Using this model, case studies were conducted in three locations in India, each location having two different demands. The results are presented. With the optimal rating of HPS, results shows that, conventional energy cost is higher.     

Conditions for a 100% renewable energy supply system in Japan and South Korea

In the wake of the Fukushima nuclear accident, alternative energy paths have been discussed for Japan, but except for a few studies the assumption is usually made that Japan is too densely populated to be suited for a near-100% sustainable, indigenous energy provision. The studies emphasizing renewable energy have proposed the use of photovoltaic power as the main source of electricity supply, in combination with diurnal battery storage and supplemented by other renewable sources such as wind, hydro, and geothermal power. Here, an alternative approach is explored, with wind and derived hydrogen production as the main energy source, but still using solar energy, biofuels, and hydropower in a resilient combination allowing full satisfaction of demands in all sectors of the economy, i.e., for dedicated electricity, transportation energy as well as heat for processes and comfort. Furthermore, the possible advantage of establishing a regional energy system with energy interchange and coordinated management of the mix of renewable energy resources across a wider region is discussed. As the closest neighbor, the energy system of South Korea is considered, first regarding the possibility of a similar full renewable energy reliance, and then for possible synergetic effects of connecting the Korean and the Japanese energy systems, in order to be able to better cope with the intermittency of renewable energy source flows.     

Optimal Design Methods for Hybrid Renewable Energy Systems

Renewable and hybrid energy systems (HESs) are expanding due to environmental concerns of climate change, air pollution, and depleting fossil fuels. Moreover, HESs can be cost effective in comparison with conventional power plants. This article reviews current methods for designing optimal HESs. The survey shows these systems are often developed on a medium scale in remote areas and stand-alone, but there is a global growing interest for larger scale deployments that are grid connected. Examples of HESs are PV–wind–battery and PV–diesel–battery. PV and wind energy sources are the most widely adopted. Diesel and batteries are often used but hydrogen is increasing as a clean energy carrier. The design of an efficient HES is challenging because HES models are nonlinear, non-convex, and composed of mixed-type variables that cannot be solved by traditional optimization methods. Alternatively, two types of approaches are typically used for designing optimal HESs: simulation-based optimization and metaheuristic optimization methods. Simulation-based optimization methods are limited in view of human intervention that makes them tedious, time consuming, and error prone. Metaheuristics are more efficient because they can handle automatically a range of complexities. In particular, multi-objective optimization (MOO) metaheuristics are the most appropriate for optimal HES because HES models involve multiple objectives at the same time such as cost, performance, supply/demand management, grid limitations, and so forth. This article shows that the energy research community has not fully utilized state-of-the-art MOO metaheuristics. More recent MOO metaheuristics could be used such as robust optimization and interactive optimization.     

Can Desalination and Clean Energy Combined Help to Alleviate Global Water Scarcity?

The major present hindrance in using desalination to help alleviate global water scarcity is the cost of this technology, which, in turn is due to energy cost involved. This study examines historical trends in desalination and breaks up the cost of desalination into energy based and nonenergy based. It then develops the learning curves (relationship between cumulative production and market price) for desalination. Assuming that the photovoltaic (PV) technology will be the dominant form of energy used in the desalination process, the existing PV learning curve and desalination learning curve are combined to explore the viability of large‐scale adoption of desalination in the future. The world has been divided into seven regions and it is assumed that water demand from desalinated water will be met only within the 100‐km coastal belt. It is shown that, in most of the regions, other than sub‐Saharan Africa, Central America, and South Asia (where water tariffs are low), the desalination (without considering energy) becomes viable by 2040. For PV technology, less than 1 million MW per annum growth is required till 2050 to make it affordable. Globally, desalination with renewable energy can become a viable option to replace domestic and industrial water demand in the 100‐km coastal belt by 2050.     

Photovoltaic-thermal (PV/t) panel to minimize electrical and air conditioning energy consumption of a typical office in Beirut

A combined photovoltaic–thermal (PV/t) panel is proposed to produce simultaneously electricity and heat from one integrated unit. The unit utilizes effectively the solar energy through achieving higher PV electrical efficiency and using the thermal energy for heating applications. To predict the performance of the PV/t at a given environmental conditions, a transient mathematical model was developed. The model was integrated in a heating application for a typical office space in the city of Beirut to provide the office needs for electricity, heating during winter season, and dehumidification and evaporative cooling during the summer season. To minimize the yearly office energy (electrical and heat) needs, the PV/t panel cooling air flow rate and the dehumidification regeneration temperature were determined for opimal unit operation. Thermal energy savings of up to 85% in winter and 71% in summer were achived compared to conventional systems at a payback period of 8 years for the panels.     

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.     

Multi-objective-based optimal transmission switching and demand response for managing congestion in hybrid power systems

ABSTRACT

This paper proposes a novel congestion management (CM) approach by using the optimal transmission switching (OTS) and demand response (DR) for a system with conventional thermal generators and renewable energy sources (RESs). In this paper, wind and solar PV units are considered as the RESs. The stochastic behavior of wind and solar PV powers are modeled by using the appropriate probability density functions (PDFs). The proposed CM methodology simultaneously optimizes the generation dispatch, demand response, and also the network topology of the power system. The OTS identifies the branches that should be taken out of service by significantly reducing the operating cost of the system while respecting the system security. Here, the total operating cost minimization/social welfare maximization and system losses minimization are considered as the objectives to be optimized. The proposed CM problem is solved using the multi-objective Jaya algorithm and it is used to determine a set of Pareto-optimal solutions. The Jaya algorithm is simple and it does not have any algorithmic-specific parameters to be tuned. This aspect reduces the designer’s effort in tuning the parameters to arrive at the optimum objective function value. A fuzzy logic-based approach is used to identify the best compromise solution. The effectiveness of the proposed CM approach is examined on modified IEEE 30 and practical Indian 75 bus test systems. The obtained simulation results are analyzed and they show the effectiveness of the proposed approach.     

The impact of the electricity tariff reform on renewable energies and energy efficiency investments: The case of the Italian residential market

This article investigates the impact that the electricity tariff reform is likely to have on investments in renewable energies (i.e., photovoltaics) and the adoption of energy efficiency measures (i.e., installation of heat pumps and efficient home appliances) in the residential market in Italy. The study develops detailed cost comparisons and simulations considering two different investment scenarios (before and after the reform) to conclude that the reform will: (i) have a negative impact on investments in photovoltaic systems; (ii) favor the adoption of energy efficiency measures, such as efficient home appliances.     

Investigation of the effects of battery types and power management algorithms on drive cycle simulation for a range-extended electric vehicle powertrain

Fuel cell (FC) hybrid vehicle power trains are an attractive technology especially for automotive applications because of their higher efficiency and lower emissions compared to conventional vehicles. This study focuses on the design of an FC hybrid power train system and evaluation of its simulations for a given speed profile through two alternative power management algorithms (PMAs). Parameters suitable for a small vehicle were taken into consideration in the mathematical model of the vehicle. The proposed hybrid power train consists of an energy storage system, composed of a 4-kg battery pack (either lithium-ion (Li-ion) battery, nickel metal hydride, or nickel–cadmium) and a direct methanol fuel cell (DMFC) as the range extender. The PMAs basically aim to fulfill the power requirements of the vehicle and decide how to command the power split between the battery and the FC. The model comprising a DMFC, a battery, and PMAs was developed in MATLAB/Simulink environment. The polarization curve of the FC was obtained using a one-dimensional DMFC model. Vehicle power requirements for a drive cycle were calculated using the equations of longitudinal dynamics of vehicle, and the results were integrated into MATLAB/Simulink model. As a result of the simulations, methanol consumption, state of charge of the battery, and power output of the FC were compared for the PMAs. This comparison shows the effect of PMAs on the hybrid vehicle performance for three battery types. The results indicate that the vehicle range could be increased when proper strategy is used as PMA.     

Comparison study and parameter identification of three battery models for an off-grid photovoltaic system

There is growing interest in solar batteries, especially for photovoltaic (PV) applications. Therefore, an accurate battery model is required for the PV system because of its influence on system efficiency. Several mathematical models of batteries have been described in the scientific literature. However, this paper reviews three electrochemical models most commonly used for PV systems, such as Shepherd, Manegon and Coppetti, in order to define the most appropriate model for PV systems. This paper discusses an application of the pattern search optimization technique to extract the parameters of three battery models derived from experimental test results obtained from sealed gelled lead acid batteries for both charge and discharge modes. A comparative case and regression analysis based on statistical tests and a quantitative method were conducted to demonstrate the effectiveness and accuracy of the updated model from the three aforementioned. The simulation results and tests performed on the battery charge and discharge modes lead us as well to approve the algorithm’s accuracy regarding the updated model.     

Higher-efficiency for combined photovoltaic-thermoelectric solar power generation

Solar energy application in a large spectrum has the potential for high-efficiency energy conversion. Though, solar cells can only absorb photon energy of the solar spectrum near their band-gap energy, and the remaining energy will be converted into thermal energy. The use of the thermoelectric generator becomes a necessity for convert this thermal energy dissipated so as to increase efficiency conversion.

This paper analyses the feasibility of photovoltaic-thermoelectric hybrid system and reviews their performance in order to optimize harvested energy. Regarding the thermoelectric effect, a new method of the ambient energy harvesting is presented. This method combines thermoelectric generators and the effects of heat sensitive materials associated to photovoltaic cells in phase change for generating both energy day and night. Experimental measures have been conducted primarily in laboratory conditions for a greater understanding of hybridization phenomena under real conditions and to test the actual performance of devices made. Results show that the hybrid system can generate more power than the simple PV and TEG in environmental conditions. This hybrid technology will highlight the use of renewable energies in the service of the energy production.     

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.