Multiple Renewable Resources Research Articles

Design optimization of a grid-tied microgrid for a residential community in southern Bangladesh

Abstract Growing energy demand, diminishing fossil fuel reserves and geopolitical tensions are serious concerns for any country’s energy strategy and security. These factors have a greater impact on developing countries, as many of them rely largely on traditional energy resources. Cleaner energy generation is the viable alternative for mitigating these problems, as well as achieving energy independence and tackling climate change. The article discusses planning and design optimization of a residential community microgrid based on multiple renewable resources. In particular, the design and techno-economic assessment of a grid-tied hybrid microgrid for meeting the electricity demand of an alluvial region, Urir Char, located in southern Bangladesh, was addressed. Hybrid Optimization of Multiple Energy Resources is used for the evaluation and it is supplemented by a fuzzy-logic-based load profile design strategy. In addition to the analysis, a predictive load-shifting-based demand management is also introduced. Several cases were considered for the studies and, after considering several criteria, a grid-tied system comprising a photovoltaic array, wind turbine and energy storage system was found to be the best fit for powering the loads. The suggested system reduces the life-cycle cost by 18.3%, the levelized cost of energy by 61.9% and emissions by 77.2% when compared with the grid-only option. Along with the microgrid design, cooking emissions and energy categorization were also discussed.

Reliable operation of an optimal self-scheduling multicarrier system with two alternatives: Multiple renewable utilities or hydrogen facilities

Multicarrier systems (MCSs) have been retrofitted with storage facilities to enhance their resilience to meet the fluctuating demands. Considering the complexity of the systems and the interactions among various carriers, optimal resource allocation is a serious challenge in the sustainable management of energy systems. This study proposes a new self-scheduling MCS to supply power, heating, cooling, and freshwater, without external cooling and nonrenewable heating facilities. Modeled based on circular integration principle, the MCS consists of various subsystems and storage facilities to treat the effluent and digest excess sludge of a wastewater treatment facility. The MCS is optimally designed and scheduled in a two-stage multi-objective optimization to satisfy the demand loads of a small community in two configurations: one with a material conversion unit (MCS-MC) to store and utilize hydrogen, and the other with weather-driven renewable energy units (MCS-RE) for a reliable operation. Accordingly, total annual cost (TAC), total exergy dissipation (nTED), and the mismatch between supply and demand (MSD) are minimized in a multi-objective optimization problem to obtain the optimal capacity of the equipment and the short-term storage facilities are scheduled in the first and second stages, respectively. The results show that the optimal MCS-MC and MCS-RE configurations reduced the TAC, nTED, and MSD by 30.2% and 12.4%, 32.9% and 23.4%, and 55.4% and 48.5%, respectively, compared to the base system. The new utility-free hydrogen-assisted multi-carrier systems are anticipated to substitute present systems with multiple renewable resources.

An iterative auction for resource-constrained surgical scheduling

We consider a decentralized surgical scheduling problem with conflicting multiple renewable resources requirements under surgeon’s private availability restriction. The objective of the surgical scheduling planner is to maximize the weighted sum of the number of potential selected surgeries. Due to the information asymmetry and the efforts taken by self-interest decision makers to maximize their own benefits, the coordination between the surgical schedule planner and surgeons for achieving efficient scheduling is difficult. In order to solve the decentralized surgical scheduling problem, we propose an auction mechanism in which surgeons only need to submit partial availability information when necessary. In the mechanism, surgeons submit bids based on their availability and renewable resources requirements for performing a surgery, while the winner determination model is formulated to select bids when the termination condition of the iterative bidding procedure is not satisfied. Under the proposed mechanism, we prove that myopic bidding is surgeons’ weakly dominant strategy. The results of our computational experiments also show that the proposed mechanism can achieve high efficiency compared with optimal solutions on different supply-demand ratio configurations. We also observed that the privacy loss incurred during the bidding process is negatively correlated to the supply-demand ratio.

Constructing a versatile hybrid harvester for efficient power generation, detection and clean water collection

Exploring renewable green energy and clean water resources is a significant and long-term measure to guarantee the sustainable development of future society. At present, developing hybrid devices that can continually produce electricity and fresh water are particularly in demand. Herein, a fiber-shaped hybrid harvester composed of a water collect component with contrasting wettability surface and liquid-solid triboelectric component is proposed, which can efficiently extract fresh water from a moist environment and convert water drop energies into electrical power at the same time. By systematically manipulating the dopamine/lysine mass ratio, proportion of alternating superhydrophilic/hydrophobic surface, electrode length, and tribolayer thickness, the optimized integrated hybrid device provides the water collection efficiency of 3.2 g/h and generate the current of 41.5 nA and output power of 0.23 μW. Moreover, this device can maintain desirable durability and stability of water collection and power generation even after various deformation, soaking, and long-time exposure. Due to these advantages, the harvester can be used as the power supply for charging various capacitors and also as a self-powered sensor for intelligent environmental detection systems, such as monitoring fog intensity, the volume/frequency of raindrops, and the pipe water leakage. This work not only realizes the perfect combination of fresh water collection and electricity production but also provides novel insight to develop versatile hybrid harvester devices for obtaining multiple renewable resources.

Optimal Energy Management Algorithm for Smart Cities Using Online Energy Trading Framework

The latest and advance renewable power equipment being deployed in smart cities have increased the overall power generation efficiency. Due to the penetration of internet of things (IoT), the online energy trading (OET) methods in Smart Grids (SG) have significantly enhanced the power generation from multiple renewable resources and enabled the bidirectional flow of power between distributed generators (DGs). Previous research works on OET mainly focus on the optimization of central grid or enhancing the decisional capabilities of central OET operators. However, few of them developed the optimal decision taking methods for a single consumer in the OET structure, but these methods could not be implemented practically. In this paper we have proposed a mixed-integer-linear programming’ (MILP’) method for a consumer who have domestically installed renewable power plant, owns electric vehicle (EV), battery storage (BS) system, and uses OET based energy purchasing and sealing structure to minimize energy loss. In this method we have considered an actual power network for optimizing the EV and BS charging as well as managing different power utility facilities on the consumer end by OET, which enable a consumer to buy and sale electricity through OET network. In this method the energy cost is pre-set by the power suppliers for all OET platforms. Practically, the MILP’ is NP’ complete therefore, the proposed method is comprised of several problems and variables. To cope this issue, in this paper we have proposed a genetic algorithm (GA) this GA consists of a novel repairing framework to tackle the optimal solution infeasibilities for all contingencies. The simulation results show the proposed algorithm can efficiently reduce power loss on the consumer end.

Centralized protection scheme for smart grid integrated with multiple renewable resources using Internet of Energy

Transmission and subtransmission lines in smart grid systems are designed as multitapped lines to supply loads or integrate wind or solar farms as medium-sized generation technologies. Transmission lines with multiple taps are an economical solution to prevent building new substations. However, these types of tapped lines create operational and protection challenges. When renewable resources are added to feeders, they act as transmission lines where energy flows in either direction in protection relays. Then, fault current flows in different directions and the coordination protection for this configuration fails. Modern protection schemes based on standalone decisions are not suitable in such cases, and new system protection methods should be devised. A key factor for future energy supply is information and communication technology based on the Internet of Energy (IoE), which is utilized to exchange information among all measurements on transmission and subtransmission systems in an environmental domain for accurate unit protection and the optimization of the coordination process. This information is collected by employing integrated electronic devices (IEDs). In such cases, the data measured by IEDs and exchanged in the environmental domain using IoE can achieve accurate unit protection for optimizing the coordination process. This system is a new development in the protection field using informatics; it is named i-protection. i-protection provides a novel method of accurately detecting faults using IEDs and IoE through wide-area wireless networks such as the IEEE 802.16 WiMAX technology. This is the first study to apply i-protection for protecting complex configurations with multiple taps at two different levels, i.e., transmission and subtransmission. The protection method considers the studied power system as one zone rather than multiple separate protection zones, which are used by conventional protection. The results show that the proposed protection method can successfully detect all types of faults in complex multitapped transmission lines with renewable resources. Moreover, the initial results indicate that the IEEE 802.16 network can meet the latency requirement of the proposed protection scheme.

Supervisory control and data acquisition for Standalone Hybrid Power Generation Systems

Recently, the development of renewable energy resources has increased significantly. In particular, hybrid power combines multiple renewable resources and the next generation of systems with diverse micro-controllers and sensors has become a common trend. Since the hybrid electric power generation systems are usually located remotely and have various micro-controllers and sensors to be acquired and processed, a SCADA (supervisory control and data acquisition) system is required to monitor them remotely and control the data from the various sensors. The SCADA system collects data from distributed sensors to provide real time information for controlling micro-controllers of single or multiple turbines.In this paper, we present the design and implementation of the SCADA which is an integral part of energy operation for a standalone offshore wave-wind hybrid power generation system. The hybrid power generation system has four 2MW-class wind turbines and twenty-four 100KW-class wave force generators. The SCADA system is designed based on IEC61850 which is an international standard for vendor-agnostic engineering of the configuration of Intelligent Electronic Devices for electrical substation automation systems for communicating with each generator. We also show that the designed SCADA system is executed properly according to the command of the transmission system's operator in a simulation-based testing environment.

Photurgen: The open source software for the analysis and design of hybrid solar wind energy systems in the Caribbean region: A brief introduction to its development policy

Abstract Hybrid Renewable Energy Systems (HRES) use multiple renewable resources such as hydro, solar and wind collaboratively to produce energy that can meet a defined load demand continuously. Their combination can lead to the improvement in the systems efficiency and overall reliability. However, the level of penetration of HRES in the Caribbean region is less than its expected potential. The constraints generated by their complexity and the costly access to useful energy planning tools is a limitation to their implementation. Therefore, in collaboration with the Alternative Energy Research Group, UWI Mona, we develop a free Linear Optimization software, Photurgen, for the design and analysis of hybrid solar-wind systems within the Caribbean region. Solar-wind hybrid systems are simulated based on historic climatological resources and instantaneous load consumption data, providing the user with graphics and advice for their optimal configuration. This paper introduces the first version of Photurgen and its associated development policies. This tool is one simple solution to be applied to increase the rate of autonomous and grid-tied households within the region, with Jamaica being its experimental location.

Adaptive Critic Design-Based Dynamic Stochastic Optimal Control Design for a Microgrid With Multiple Renewable Resources

This paper proposes a three-layer optimization and an intelligent control algorithm for a microgrid with multiple renewable resources. A dual heuristic dynamic programming-based system control layer is used to ensure the dynamic performance and voltage dynamics of the microgrid as the system operation conditions change. A local layer maximizes the capability of the photovoltaic (PV) wind power generators and battery systems, and a model predictive control-based device layer increases the tracking accuracy of the converter control. The proposed control scheme, system wide adaptive predictive supervisory control (SWAPSC) smooths the output of PV and wind generators under intermittencies, maintains bus voltage by providing dynamic reactive power support to the grid, and reduces the total system losses while minimizing degradation of battery life span. Performance comparisons are made with and without SWAPSC for an IEEE 13 node test system with a PV farm, a wind farm, and two battery-based energy storage systems.

Multi-class, multi-resource advance scheduling with no-shows, cancellations and overbooking

We investigate a class of scheduling problems where dynamically and stochastically arriving appointment requests are either rejected or booked for future slots. A customer may cancel an appointment. A customer who does not cancel may fail to show up. The planner may overbook appointments to mitigate the detrimental effects of cancellations and no-shows. A customer needs multiple renewable resources. The system receives a reward for providing service; and incurs costs for rejecting requests, appointment delays, and overtime. Customers are heterogeneous in all problem parameters. We provide a Markov decision process (MDP) formulation of these problems. Exact solution of this MDP is intractable. We show that this MDP has a weakly coupled structure that enables us to apply an approximate dynamic programming method rooted in Lagrangian relaxation, affine value function approximation, and constraint generation. We compare this method with a myopic scheduling heuristic on eighteen hundred problem instances. Our experiments show that there is a statistically significant difference in the performance of the two methods in 77% of these instances. Of these statistically significant instances, the Lagrangian method outperforms the myopic method in 97% of the instances.

Multi-Class, Multi-Resource Advance Scheduling with No-Shows, Cancellations and Overbooking

We investigate a class of scheduling problems where dynamically and stochastically arriving requests for appointments are either rejected or booked for future slots. A customer may cancel an appointment. A customer who does not cancel may, with some probability, fail to show up. The planner may overbook appointments to mitigate the detrimental effects of cancellations and no-shows. A customer needs multiple renewable resources while in service. The system receives a reward for providing service to a customer; and incurs a cost of rejecting requests, a cost for appointment delays, and a cost of overtime. Customers are heterogeneous in their arrival patterns; costs and rewards; resource consumptions; and cancellation and no-show behaviors. Such advance scheduling problems arise in healthcare, revenue management, manufacturing, telecommunications, civilian and military logistics, and high performance computing. We provide a Markov decision process (MDP) formulation of these problems. Owing to the large state- and action-spaces in this MDP, its exact solution is intractable. We show that this MDP has a special, weakly coupled structure. This enables us to apply an approximate dynamic programming method that is rooted in Lagrangian relaxation, affine value function approximation, and constraint generation. We compare the performance of this method with a myopic scheduling heuristic on eighteen hundred randomly generated problem instances. Our experiments show that there is a statistically significant difference in the performance of the two methods in 77% of these instances. Of these statistically significant instances, the Lagrangian method outperforms the myopic method in 97% of the instances.

Scheduling of Multimodal Activities with Multiple Renewable and Availability Constrained Resources under Stochastic Conditions

Abstract We address the stochastic multi-mode resource-constrained project scheduling problem in the case where the project activities require multiple renewable resources, constrained by total availability. In our study, we introduce uncertainty by characterizing, in each activity, each required resource with a work content following some known distribution. Furthermore, the set of feasible resource allocations is a continuous and well defined interval of real values. In turn, any allocation must also comply with the resource maximum total availability at any point during the project execution. We propose a mathematical model that relies on a discrete and finite partition of the timespan when evaluating activity schedules and enforces equal durations yielded by each required resource, within each activity, in expectation. The objective is to determine the start times and the mode (defined by the resource allocations) which minimize the expected total project cost. We implemented a method relying upon two global optimization metaheuristics - the electromagnetic-like mechanism and the evolutionary algorithm to solve the problem.

Poly(tetramethyl glycolide) from Renewable Carbon, a Racemization-Free and Controlled Depolymerizable Polyester

The development of biomass-based polymers is one strategic step toward achieving a sustainable social system. A typical biomass-based polymer is poly(L-lactic acid) (PLLA), which through a combination of biological fermentation and chemical reaction can be synthesized from naturally abundant biomasses suchas starchor cellulose. PLLAshows goodphysical properties such as crystallinity, thermoplasticity, transparency, and a high melting point (Tm) of around 170 C. It also has the excellent quality of being easily reproduced from the depolymerization product: L,L-lactide. Hence, the likelihood that PLLA will become the plastic material of choice for sustainable systems has been attracting much interest from researchers. However, in practical applications, PLLA does have some drawbacks, such as slow crystallization, low impact resistance, hydrolyzability, and racemization. PLLA readily causes racemization from an L-unit to a D-unit in a chain under heating. Such racemization proceeds by the mechanism of ester-semiacetal tautomerization, causing a decrease in optical purity and crystallinity. This is a serious problem in the reproduction of practical materials via thermal depolymerization and repolymerization. A fundamental and complete solution to this problem requires a modification of the chemical structure of lactic acid. In this study, in order to overcome the problems associated with PLLA while preserving its superior properties, a biomass-based and racemization-free polymer: poly(tetramethyl glycolide) (PTMG) possessing superior depolymerizability for the reproduction is developed. Previously, PTMG has been synthesized from petroleum by wholly chemical processes involving the ring-opening polymerization of tetramethyl glycolide (TMG), which is a cyclic dimer of R-hydroxyisobutyric acid (HIBA). HIBA itself has also required preparation over many steps from petroleum using the cyanhydrin method for methyl methacrylate production. The methyl methacrylate production has been improved by some novel production processes such as the AVENEER method. Recently, a biosynthesis method of HIBA from renewable carbons has been achieved. PTMG shows a highTm at 185-190 C and a characteristic thermal degradability into methacrylic acid, TMG, acetone, etc. However, the derivation of PTMGfrombiomass and its controlled depolymerization into monomers, which will become required for many commonly used polymers in a future, are newly proposed in this study. Renewable resources: D-/L-lactic acids and pyruvic acid derived from biomasses are employed as starting materials for the synthesis of HIBA in this study, which is an acyclic monomer of PTMG. The biomass-based HIBA is prepared bymethylation of the acids and then converted into the cyclic dimer: TMGby a cyclic esterification. The following synthesis of polymer PTMG is carried out by a ring-opening polymerization of TMG. Moreover, the controlled depolymerization of PTMG is performed either to return toTMG or to convert to methacrylic acid depending on the use of a specific catalyst for each monomeric product. Two synthetic routes of HIBA from the renewable resources were performed. One was the direct methylation of D-/ L-lactic acid derivatives after the abstraction ofR-hydrogen on a chiral carbon: the other was themethylation of an R-keto group of apyruvic acid derivative by theGrignard reaction,which is an oxidized form of corresponding D-/L-lactic acid derivatives. Although the direct methylation has been reported in our recent research, themethylationof thepyruvic acidderivative is anew finding introduced in this study. Results of the methylation are listed in Table 1. The methylation of methyl pyruvate by the Grignard reaction showeda50%yield at room temperature.On the other hand, the direct methylation of the hydroxyl-group protected ethyl D-/L-lactate (HPEL), with protection provided by a methoxymethyl group, gave relatively high yields of 5475%at-84 C.Thesemethylation reactionshave the advantage of using multiple renewable resources: pyruvic acid and L-, D-, and D-/L-lactic acids without the need of high optical purity. HIBA was obtained by the hydrolytic deprotection of methylated products with high yields of >74%. The cyclic dimerization ofHIBAproceeded smoothly in the presence of the dehydration catalyst methane: sulfonic acid to isolate the cyclic dimer: TMG in a 67% yield. The ring-opening anionic polymerization of TMG that followedwas achieved by using three initiators: EtOLi, n-BuLi, and t-BuLi, resulting in the preparation of a high molecular weight PTMG (Mn 90 000) as shown in Table S1. Previously, Deibig et al. showed Tm of PTMG in a range of 180-190 C, but no glass transition (Tg) temperature was reported. The isolated PTMG showed Tm and Tg at 191 and 70 C (Figure 1), respectively, about 15 C higher than those of PLLA. TheTg transition signal was very weak, and theTm peak shifted into higher temperatures of up to 206 C with a corresponding increase in the heat treatment temperature. The weak Tg signal and high Tm value of PTMG suggest superior crystallization and heat resistance, respectively. Scheme 1. Total Synthetic Processes of PTMG and Its Depolymerization

Improved genetic algorithm for resource-constrained scheduling of large projects

The generalized model of the resource-constrained project scheduling problem (RCPSP) is valuable because it can be incorporated into the advanced computational methods of commercial project management software for practical applications. A construction schedule generated by most commercial project management programs does not guarantee its optimality when the resources are limited. This paper presents an improved elitist genetic algorithm (GA) for resource-constrained scheduling of large projects. The proposed algorithm allocates multiple renewable resources to activities of a single large-sized project to achieve the objective of minimizing the project duration. A permutation-based decoding procedure is developed using the improved parallel schedule generation scheme. A new parameter, named transformation power, is created in the transformation method of the improved algorithm to ensure that the individual selection process performs correctly. Extensive computational results using a standard set of large-sized multiple resource-constrained project scheduling problems are presented to demonstrate the performance and accuracy of the algorithm.

Minimizing Resource Availability Costs in Time-Limited Project Networks

We consider the problem of minimizing renewable resource availability costs in an activity-on-the-node project network subject to a project due date. Project activities have fixed durations and may require the use of multiple renewable resources in constant amounts throughout their duration. Various assumptions may be made about the type of precedence relations, ready times, due dates, and task interruptability. Given a discrete, non-decreasing cost function of the constant resource availability for every resource type, the objective is to determine the resource availability levels in order to minimize the sum of the availability costs over all resource types. An effective optimal algorithm is described and extensive computational experience is reported.