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.     

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.     

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.     

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.     

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.     

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.     

Economic dispatch of power system containing wind power and photovoltaic considering carbon trading and spare capacity variation

The drying up of the fossil energy sources and the damage from unchecked carbon emissions demand the development of low carbon economy, which promotes the development of new energy sources, such as wind power and photovoltaic. However, the direct connections of wind/photovoltaic power into power grid bring great impacts on power systems, thus affecting the security and stability of power system operations, which challenges the power system dispatching. In despite of many methods for power system dispatch, lack of the models, for power system containing wind power and photovoltaic considering carbon trading and spare capacity variation (PSCWPCCTSCV), restricts the further optimal operations of power systems. This paper studies the economic dispatch modeling problem of power system containing wind power and photovoltaic, establishes the model of economic dispatch of PSCWPCCTSCV. On this basis, adaptive immune genetic algorithm is applied to conduct the economic operation optimization, which can provide the optimal carbon trading price and the optimal power distribution coefficient. Finally, simulations based on the newly proposed models are made to illustrate the economic dispatch of PSCWPCCTSCV. The results show that optimization with the proposed model can not only weaken the volatility of the new energy effectively, but also reduce carbon emissions and reduce power generation costs.     

Wind energy and natural ventilation potential of a wind catcher in Yazd – Iran (a long-term measurement)

When designing energy efficient buildings it is useful to study existing climate—responsive building typologies. The wind towers or wind-catchers of Yazd city in Iran are typical examples of such a typology. Although many previous studies have investigated the performance of various types of wind catcher systems, studies based on a long-term real-life measurement can be rarely found. In this study a long-term whole year monitoring campaign on an existing full scale four sided wind catcher in Yazd was carried out in 2014–2015. Three prevailing wind directions were identified and the measured on-site wind speeds were used to estimate the wind induced natural ventilation potential of the tower. A shaft/tower airflow performance index was developed. The monitoring results were compared to the ASHRAE Standard 62:2001 ventilation rate requirement. Results show that the total ventilation rate of the wind tower surpasses the ASHRAE Standard requirements. Furthermore it shows that the shafts are exposed to the prevailing wind directions perform better. For more effective natural ventilation a wind tower with adaptable openings/shafts are proposed.     

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.     

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.     

Investigation of sustainable energy alternatives for powering remote communities in northern Ontario

ABSTRACT

Remote communities in the North of Ontario survive in isolation as their proximity to the southern industrial sector of the province limits their accessibility to the major grid. The lack of grid connection has led to antiquated methods of power generation which pollute the environment and deplete the planet of its natural resources. Aside from the primary means of electricity generation being by diesel generators, generation infrastructure is deteriorating due to age and the stagnation of the power supply has led to communities facing load restrictions. These challenges may be resolved by introducing clean energy alternatives and providing a fuel blend option. The primary energy sources investigated in this research are solar, wind, and hydrogen. To assess the viability of these energy production methods in Northern communities, an exergy analysis is employed as it utilizes both the first and second law of thermodynamics to determine systems’ efficiency and performance in the surroundings. Local weather patterns were used to determine the viability of using wind turbines, solar panels and/or hydrogen fuel cells in a remote community. Through analysis of the resources available at the community, it was determined that the hydrogen fuel cell was best suited to provide clean energy to the community. Wind resulted in low efficiency in the range of 2–3% while solar efficiencies resulted in ranges of 18 – 19%, as the seasonal variations between the three years is not very great. Due to the higher operating efficiencies observed of the PV panels it would also be an attractive alternative to diesel generators however, the lack of consistent operation above 30% efficiency throughout the year, resulted in hydrogen fuel cells being a better alternative.     

Automated energy management in distributed electricity systems: An EEPOS approach

The increasing capacity of distributed electricity generation brings new challenges in maintaining a high security and quality of electricity supply. New techniques are required for grid support and power balance. The highest potential for these techniques is to be found on the part of the electricity distribution grid.

This article addresses this potential and presents the EEPOS project’s approach to the automated management of flexible electrical loads in neighborhoods. The management goals are (i) maximum utilization of distributed generation in the local grid, (ii) peak load shaving/congestion management, and (iii) reduction of electricity distribution losses. Contribution to the power balance is considered by applying two-tariff pricing for electricity.

The presented approach to energy management is tested in a hypothetical sensitivity analysis of a distribution feeder with 10 households and 10 photovoltaic (PV) plants with an average daily consumption of electricity of 4.54 kWh per household and a peak PV panel output of 0.38 kW per plant. Energy management shows efficient performance at relatively low capacities of flexible load. At a flexible load capacity of 2.5% (of the average daily electricity consumption), PV generation surplus is compensated by 34–100% depending on solar irradiance. Peak load is reduced by 30% on average. The article also presents the load shifting effect on electricity distribution losses and electricity costs for the grid user.     

Hybrid power supply solutions for off-grid green wireless networks

Increasing deployment of cellular networks across the globe is pushing the energy consumption in cellular networks at an exceptional rate. The integration of renewable energy (RE) harvesting technology into future mobile networks has the potential to positively cope with environmental contamination and ensure self-energy sustainability as a means to decrease fossil fuel consumption. Diesel generator (DG) in conjunction with on-site RE harvester has emerged as an economic and extent efficient option where commercial grid supply is not viable. This paper is focused on the cost aware energy management framework addressing to least net present cost (NPC) for the envisioned hybrid powered green cellular base stations (BSs) considering tempo-spatial traffic dynamics. In such wireless networks, solar photovoltaic modules are considered as a primary energy source, while the DG and energy storage device are kept as the standby supply in case of inadequate solar energy to ensure zero outage. A comprehensive simulation-based investigation is carried out in the context of downlink Long-Term Evolution (LTE) cellular networks for evaluating cost-efficiency and reliability performance under a wide range of network settings. Particularly, this paper examines the energy yield, greenhouse gas emissions, and cost analysis based on the optimal architecture of Remote Radio Head-enabled LTE BS. Moreover, wireless network performance in terms of throughput, energy efficiency gain, and radio efficiency is thoroughly investigated using Monte Carlo simulations. Numerical results demonstrate a substantial reduction of carbon footprints with minimum NPC while satisfying the quality of service requirements.     

Suitable error evaluation criteria selection in the wind energy assessment via the K-means clustering algorithm

In this paper, wind energy potential of four locations in Xinjiang region is assessed. The Weibull distribution as well as the Logistic and the Lognormal distributions are applied to describe the distributions of the wind speed at different heights. In determining the parameters in the Weibull distribution, four intelligent parameter optimization approaches including the differential evolutionary, the particle swarm optimization, and two other approaches derived from these two algorithms and combined advantages of these two approaches are employed. Then the optimal distribution is chosen through the Chi-square error (CSE), the Kolmogorov–Smirnov test error (KSE), and the root mean square error (RMSE) criteria. However, it is found that the variation range of some criteria is quite large, thus these criteria are analyzed and evaluated both from the anomalous values and by the K-means clustering method. Anomaly observation results have shown that the CSE is the first one should be considered to be eliminated from the consequent optimal distribution function selection. This idea is further confirmed by the K-means clustering algorithm, by which the CSE is clustered into a different group with KSE and RMSE. Therefore, only the reserved two error evaluation criteria are utilized to evaluate the wind power potential.     

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.     

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.     

A hybrid model based on smooth transition periodic autoregressive and Elman artificial neural network for wind speed forecasting of the Hebei region in China

Wind energy, one of the most promising renewable and clean energy sources, is becoming increasingly significant for sustainable energy development and environmental protection. Given the relationship between wind power and wind speed, precise prediction of wind speed for wind energy estimation and wind power generation is important. For proper and efficient evaluation of wind speed, a smooth transition periodic autoregressive (STPAR) model is developed to predict the six-hourly wind speeds. In addition, the Elman artificial neural network (EANN)-based error correction technique has also been integrated into the new STPAR model to improve model performance. To verify the developed approach, the six-hourly wind speed series during the period of 2000–2009 in the Hebei region of China is used for model construction and model testing. The proposed EANN-STPAR hybrid model has demonstrated its powerful forecasting capacity for wind speed series with complicated characteristics of linearity, seasonality and nonlinearity, which indicates that the proposed hybrid model is notably efficient and practical for wind speed forecasting, especially for the Hebei wind farms of China.