Generation Of Renewable Resources Research Articles

Roles of MXenes in Photocatalysis

Artificial photocatalysis is beneficial to carbon neutrality, renewable resources generation, and sustainable society development. Due to the unique layered structure, abundant surface functional groups, and tunable electronic properties, MXene is emerging as a promising contender to improve the comprehensive performance of photocatalysts. Much progress has been made in the study of MXene‐based nanomaterials over the past years, and state‐of‐the‐art advances in the rational design, controllable synthesis, characterization techniques, and diverse applications of MXene‐based photocatalysts have been reviewed. However, the roles and perspectives of MXenes in photocatalysis have been less summarized. Based on the analysis of the physical/chemical properties of MXenes and the latest research progress, herein, the multiple roles of MXenes are discussed in promoting photocatalysis, including carrier for active site anchoring, light‐transmitting media, mass transfer medium, cocatalyst, and heat conductor. In addition, the current status and challenges of MXenes in photocatalysis are summarized, and the future directions as well as the bottleneck of development that need to be broken through for MXenes in photocatalysis are also proposed. This review provides a clear and in‐depth understanding of the physical/chemical properties of MXene‐based photocatalysts and their contributions to photocatalysis.

Optimal operation of smart energy hub considering high-temperature heat and power storage

Due to the impacts of high intermittent renewable resources, increasing their penetration in the generation sector of energy systems is still challenging. In this regard, the integration of different energy networks can play a key role in dealing with this challenge. This paper focuses on the analysis and optimization of multi-generation energy storage (MGES) system’s performance and investigates the role of this type of energy storage in the operation of future smart energy systems by considering 100% green energy goals. Hence, this paper first introduces high-temperature heat and power storage (HTHPS) system, as a novel MGES unit, for a local integrated energy system (IES) with different energy carriers. Then, a novel optimal energy scheduling scheme for this system is presented by considering the energy hub concept. The proposed IES is introduced as a Smart Energy Hub (SEH) which supplies local energy demands through local renewable resources generation, as well as upstream energy networks, by considering a competitive energy market. Finally, the performance of the proposed SEH is investigated by simulating different case studies and its effectiveness is proved in increasing the renewable generation penetration.

A novel adaptive robust model for scheduling distributed energy resources in local electricity and flexibility markets

Aggregators, as intermediaries between consumers, prosumers, and local market operators, manage their clients’ resources for participation in the multi-markets. Various resources of consumers and prosumers have different technical characteristics, which shall be considered in the scheduling strategy. The fast response and controllability of some resources may increase the profit of aggregators and improve the flexibility of distribution grids. Therefore, aggregators need a scheduling strategy to determine their participation level in different markets. However, the expected profit of aggregators can be affected by uncertainties in some resources such as PV generation or consumption of prosumers. This work provides a decomposed bi-level strategy approach for aggregators to deploy the resources of prosumers in the local energy and flexibility markets. The upper and lower sub-problems determine the optimal commitment status of the aggregator’s resources and the participation level in the local energy and flexibility markets, respectively. In the proposed model, the adaptive robust optimization (ARO) is addressed to model uncertainties of consumption and generation of renewable resources based on the worst-case realizations of uncertain parameters. In addition, impacts of consumption control programs such as load-shifting and load reduction are considered in the proposed model. Through a case study and different scenarios, the performance and efficiency of the proposed model are validated.

Supplying the energy of islands with geopolitical positions using distributed generations to increase energy security

The islands usually face challenges in their energy supply due to their specific location. On islands that are distant from the land, power is typically provided by diesel generators. Therefore, oil tankers must regularly refuel the island in order to supply the necessary fuel for diesel generators. As a result, if the fueling procedure is not completed for whatever reason, the island will not receive the necessary energy, which will result in an unavoidable loss of load. Due to their strategic location, some of these islands are used as military islands to protect the nations and waterways. Given the vital role that these military islands play for nations, a delay in supplying the island with energy can seriously harm security, the economy, and other factors. Transferring fuel to these islands is typically difficult, and in some cases impossible, under certain circumstances, such as war. Therefore, reducing the island's reliance on fossil fuels as much as feasible is vital to ensure the energy security of these specific islands. Diesel generators provide electricity to Larak Island, which is situated in the Strait of Hormuz. Larak Island serves as a military island due to its geopolitical location, hence it is crucial to consistently provide Larak with electricity. Therefore, in this paper, a combination of distributed generations and system storage is used to supply the Larak island. The photovoltaic, wind and tidal plants are considered the main power plants, and fuel cells with electrolyzers and hydrogen tanks have also been used as storage systems. In addition, the diesel generator is considered the system backup. The considered objective functions to design and manage Larak island's power supply system are reducing diesel generators fuel consumption, reducing electricity cost, and reducing electricity outages and lost power generation of renewable resources.

Market-based Real Time Congestion Management in a Smart Grid Considering Reconfiguration and Switching Cost

Network Real-Time Congestion (RTC) is a bottleneck that limits energy transfer from the generation units or up-grid to the loads. Some factors, such as intermittent generation of renewable resources and forced outages of generating units and load forecasting errors, can lead to Real-Time Congestion Management (RTCM) in a smart grid network. RTCM is a set of methods to eliminate congestion in real-time. To implement RTCM, some approaches can be employed, including network reconfiguration by Remote Control Switches (RCS), load shedding generation and up-grid power rescheduling. In this paper, a two-stage programming model is proposed to find the optimal solution for RTCM using the integration of reconfiguration and market-based approaches. Therefore, following the occurrence of congestion, at the first stage, microgrid central controller (MGCC) or central energy manager implements reconfiguration as the lowest-cost approach to mitigating RTC. The Soccer League (SL) algorithm is employed at the first stage to find the optimal network topology. Subsequently, based on the results obtained from the first stage, a programming model is applied at the second stage to completely eliminate the RTC. The proposed model minimizes a weighted objective function that includes the generation and up-grid rescheduling cost, load shedding cost, switching cost, and congestion clearing time. In order to model switching costs, a new index is defined to prevent risky switching and the depreciation caused by frequent switching. This index is determined based on the critical locations in the network and the age of RCSs. The numerical results demonstrate the efficacy of the proposed model.

A novel and comprehensive mechanism for the energy management of a Hybrid Micro-grid System

Renewable energy has gained enormous popularity in recent years. Both independent and grid-connected renewable energy generating capacity has increased. The potential to create green energy that is both ecologically benign and financially operative is the primary argument. To reduce the cost of maintaining a hybrid microgrid, this study provides a novel control algorithm as well as a suitable energy management system. The energy storage technologies are used in the hybrid microgrid system which consist of photovoltaic cell, fuel cell and supercapacitor. The optimal power flow in the hybrid microgrid component is developed using energy management system which consents control system to batteries, load and economic analysis with the input of electric grid. In this paper supercapacitor is which is directly connected to DC and provide fast response to load changing. DC voltage, current and hybrid microgrid is managed using the local control system. The power change in photovoltaic system is solved using energy storage system. The purpose of control method in hybrid microgrid is used to enhance the hybrid microgrid flexibility and generation of renewable resources and stored it in grid. The effectiveness and the flexibility of the proposed controller in energy management of hybrid microgrid system is simulated. The proposed energy management system provides high performance related to the existing energy management system.

Stochastic assessment of frequency support from wind power plants for power system with high wind penetration using correlation between wind farms

The increase of renewable energy use in a power generation decreases the flexibility of power supply. The intermittent and variable nature of renewable resources necessitates the use of a transmission system operator to prepare reserve generating power in case of power imbalance. Unfortunately, the generation and reserve margins of renewable energy generators are often insufficient. To ensure a balanced and resilient power system, surplus power generation from renewable resources must provide frequency support with the essential number of synchronous generators being remained in the network. Assessment of the amount of frequency support power from wind farm became available through forecast and real-time operation planning. However, the exact amount of back-up power that expected to be provided by individual wind farm can be barely determined due to the uncertainty and variation of renewable resources generation. A stochastic method for assessing the required frequency support operation of specific wind power plants is thus proposed in this paper. The stochastic correlation characteristics between a single wind farm and power system stability are applied in this method. The essential metrics for deloading operation and inertia response are calculated using the stochastic correlation model based on copula function.

Integration of Optimal Time-of-Use Pricing in Stochastic Programming for Energy and Reserve Management in Smart Micro-grids

In this paper, the optimal operation problem of smart micro-grids integrated with the pricing of Time-of-Use (TOU) demand response (DR) program is modeled as a two-stage stochastic programming problem with the aim of minimizing the cost of MG operation and running TOU in the presence of renewable resources and incentive-based DR programs. Here, TOU as the most common type of time-based DR programs is implemented using a linear function based on the concept of self- and cross-price elasticity of load demand. In the presented model, the forecasting errors of generation of renewable resources are modeled by probability density functions. The operator of MG decides on two stages for optimal management of its network; the first stage refers to the operation of base condition of MG and the second one is pertaining to after the realization of different scenarios for generation of renewable resources. The base condition of MG refers to the situation in which the active power productions of renewables are equal to the predicted values. The proposed model is solved by Particle Swarm Optimization algorithm. A typical MG is employed to investigate and analyze the different features of the method. By varying the demand response potential of MG consumers, TOU tariffs are determined, and their impact on the results of energy and reserve cost as well as voltage and load profiles of MG are analyzed. Numerical results show the efficiency of DR in reducing costs as well as covering the uncertainty resulting from renewables.

Risk-Constrained Bidding Strategy for Demand Response, Green Energy Resources, and Plug-In Electric Vehicle in a Flexible Smart Grid

The flexibility of smart grids has become an important issue due to the increasing penetration of uncertain energy resources, such as renewable as well as virtual power plants in the smart grids. Flexibility sources, such as demand response (DR) programs and plug-in electric vehicles (PEVs), can help the smart grid to be more productive. Although the renewable power plants are considered as flexible tools, they are somehow uncertain by themselves. In this article, the uncertainty of power generation of renewable resources has been resolved by incorporating the DR programs and PEVs. A stochastic decision making model for the coordinated operation of renewable resources and some virtual power generation is presented to solve a risk-constrained optimal bidding strategy for a smart grid. The participation of DR and PEV aggregators in the day-ahead market is considered. The uncertainty in day-ahead prices associated with renewable power generation is discussed throughout this article. As a well-known measure, the conditional value at risk is employed in the model to cope with all aforementioned uncertainties. Numerical studies and result analysis show that the expected profit of these resources is increased and the related risk is reduced significantly.

Two-step probabilistic method to enable demand response programs in renewable-based integrated energy systems

This paper presents a two-step procedure to enable residential Demand Response (DR) programs in the Multi-Carrier Energy Systems (MCES), including a combined heat and power generation, renewable resources, a boiler, and energy storage systems. In the first step, an optimization problem is solved to determine the optimal energy cost for each customer. In the second step, the system operator (SO) minimizes the summation of the total loss cost of the electrical distribution system and a designed motivational payment, subject to various operational constraints of the MCES. The motivational payment is the function of deviation of the resulted energy cost of a home in the second step from the desired cost obtained in the first step. The main idea of the proposed procedure is to consider the concerns of customers and the operator, simultaneously. This paper also addresses the uncertainties in renewable resources generation. The proposed procedure is simulated on a test energy system for various cases. The results show that as the level of customer participation in the proposed program is increased, the technical aspects of the integrated energy system are improved. In addition, the customers' costs are decreased by participation in the proposed program.

Adaptive primary droop control for islanded operation of hybrid AC–DC MGs

This study is dedicated to establish an adaptive primary droop control of interlinking converter (IC) for the islanded operation of hybrid AC–DC microgrids (MGs). In this practise, the amount of transferred power through the IC is controlled such that all of the resources in both AC and DC MGs are in equal loading per cents. To do so, two different notions are contemplated. In the first one, the IC is controlled such that the aforementioned control objective is satisfied for all possible values of transmission lines impedance angles. To this end, a new algorithm is developed within which the IC estimates the impedance angle based on the local measurements in AC MG and then adapts itself. In the second outlook, the uncertainties in association with the renewable energy resources are considered and their effects are explored on IC performance. To lessen the negative impacts of uncertainties and fluctuating power generation of renewable resources, the control of IC is updated based on the instantaneous power generation in AC and DC MGs. Accordingly, the main control objective is effectively pursued. Performance of the proposed adaptive droop control is evaluated on a test MG. Results are discussed in depth.

Net demand prediction for power systems by a new neural network‐based forecasting engine

Integration of renewable generations, such as wind and photovoltaic, into electrical power systems is rapidly growing throughout the world. Stochastic and variable nature of these resources makes some operational challenges to power systems. The most effective way to tackle these challenges is short‐term prediction of their available powers. Despite various developed methods to forecast generation of renewable resources, still they have large errors, which may lead to under/over‐commitment of conventional generators in power systems. Prediction of net demand (ND), defined as electrical load minus renewable generations, can provide useful information for accurate scheduling of conventional generators. In this article, characteristics of the time series of electric load, renewable generations and ND are analyzed, and a new hybrid prediction strategy is presented for direct prediction of ND. The training mechanism of the proposed forecasting engine is composed of a new stochastic search method and Levenberg–Marquardt learning algorithm based on an iterative procedure and greedy search. The suggested prediction strategy is tested on different real‐world power systems and its obtained results are compared with the results of several other forecast methods and published literature figures. These comparisons confirm the validity of the developed forecasting strategy. © 2016 Wiley Periodicals, Inc. Complexity 21: 296–308, 2016

Risk-based profit allocation to DERs integrated with a virtual power plant using cooperative Game theory

Distributed energy resources (DERs) play a key role in the deregulated power systems with environmental concerns. Their scales and the uncertainty pertaining to intermittent generation of renewable resources are the major challenges of participating in wholesale electricity markets. The concept of virtual power plant (VPP) makes their integration possible and also allows covering the risk due to uncertainties. It yields a surplus profit in comparison to profits made by uncoordinated DERs. In this paper, using a novel stochastic programming approach, the participation of a VPP in the day-ahead market (DAM) and the balancing (real-time) market (BM) is considered. The uncertainties involved in the electricity price, generation of renewables, consumption of loads, and the losses allocation are taken into account. The desired risk-aversion level of each independent DER owner is used to compute the conditional value-at-risk (CVaR) as a well-known risk measure. The role of each DER in covering the risk and making the total profit is evaluated. The Nucleolus and the Shapley value methods as the cooperative Game theory approaches are implemented to allocate VPP's profit to the DERs. The results of a numerical study are presented and concluded.

Joint operation of wind farm, photovoltaic, pump-storage and energy storage devices in energy and reserve markets

Renewable resources generation scheduling is one of the newest problems of the power markets. In this paper, joint operation (JO) of wind farms (WF), pump-storage units (PSU), photo-voltaic (PV) resources, and energy storage devices (ESD) is studied in the energy and ancillary service markets. There are uncertainties in wind power generation (WPG), photovoltaic power generation (PVPG) and the market prices. To model these uncertainties, the WPG is forecasted by using ARMA model and its scenarios are generated using Weibull distribution function. Moreover, other uncertain parameters are forecasted first, and their uncertainties are modeled by using scenario generation and scenario reduction method. The proposed JO method is used to determine the optimal bidding strategy of the PSU, PV, ESD and WF of IEEE 118-bus standard system. The results for these renewable energy resources confirm that the JO of these resources increases the profit and decreases the risk of the resources in comparison with their uncoordinated operation (UO).

Wastewater use in algae production for generation of renewable resources: a review and preliminary results

Microalgae feedstock production can be integrated with wastewater and industrial sources of carbon dioxide. This study reviews the literature on algae grown on wastewater and includes a preliminary analysis of algal production based on anaerobic digestion sludge centrate from the Howard F. Curren Advanced Wastewater Treatment Plant (HFC AWTP) in Tampa, Florida and secondary effluent from the City of Lakeland wastewater treatment facilities in Lakeland, Florida. It was demonstrated that a mixed culture of wild algae species could successfully be grown on wastewater nutrients and potentially scaled to commercial production. Algae have demonstrated the ability to naturally colonize low-nutrient effluent water in a wetland treatment system utilized by the City of Lakeland. The results from these experiments show that the algae grown in high strength wastewater from the HFC AWTP are light-limited when cultivated indoor since more than 50% of the outdoor illumination is attenuated in the greenhouse.An analysis was performed to determine the mass of algae that can be supported by the wastewater nutrients (mainly nitrogen and phosphorous) available from the two Florida cities. The study was guided by the growth and productivity data obtained for algal growth in the photobioreactors in operation at the University of South Florida. In the analysis, nutrients and light are assumed to be limited, while CO2 is abundantly available. There is some limitation on land, especially since the HFC AWTP is located at the Port of Tampa. The temperature range in Tampa is assumed to be suitable for algal growth year round. Assuming that the numerous technical challenges to achieving commercial-scale algal production can be met, the results presented suggest that an excess of 71 metric tons per hectare per year of algal biomass can be produced. Two energy production options were considered; liquid biofuels from feedstock with high lipid content, and biogas generation from anaerobic digestion of algae biomass. The total potential oil volume was determined to be approximately 337,500 gallons per year, which may result in the annual production of 270,000 gallons of biodiesel when 80% conversion efficiency is assumed. This production level would be able to sustain approximately 450 cars per year on average. Potential biogas production was estimated to be above 415,000 kg/yr, the equivalent of powering close to 500 homes for a year.

A Compressed Air Green Storage Device Based on the Hydro-Pneumatic Conversion and its Characteristic Analysis

Aiming to the key demands of cost, service life, environment and the time of energy storage in the energy storage technology of the distributed power generation of renewable resources, a novel compressed air green storage device based on the hydro-pneumatic conversion is put forward on the base of thermodynamics foundational principles. By the theoretical analysis for the energy cycle process of the storage device proposed, the corresponding calculation formulae are deduced for the energy storage efficiency and density under the condition of ideal gas, respectively. In addition, the changes of above index with pressure, compression ratio, and isentropic exponent are also researched. And the results show that the energy storage efficiency and density can be improved efficiently by controlling the energy conversion under the near isothermal condition so as to enhance the heat exchange capacity between the given energy storage device and its surrounding environment.