Energy Load Research Articles

Performance investigation of transitioning building services system in photovoltaic homes

Growing concern over residential carbon emissions has highlighted the necessity of transitioning conventional fossil fuel-based building services system into one that utilizes renewable energy sources. Therefore, this paper proposes a building services system, including an air-source heat pump, water storage tanks and batteries, for photovoltaic (PV) homes so that all their energy loads are electrified and mostly met by PV power. It aims to investigate the merits of the proposed system in relation to energy, economic and environmental performance by designing three distinct scenarios for power supply, space heating, space cooling, and domestic hot water (DHW). A typical Australian house equipped with a 10 kW solar PV system in Geelong, Victoria, is used as a case study. Three scenarios are modelled using Transient System Simulation (TRNSYS) software, and the simulated PV generation, space heating, and DHW load are validated using actual measured results. Findings show that the proposed system offsets all the natural gas demand of 6915 kWh required by Scenario 1 using the conventional fossil fuel-based system and increases the PV self-consumption and self-sufficiency from 18 % to 24 % in Scenario 1–38 % and 75 %, respectively. After adjusting component sizes following the principle of diminishing returns, the proposed system has a nine-year payback period and reduces carbon emissions by 67 % compared to the conventional system. This study demonstrates the capability of the proposed system to electrify our house loads, thus contributing to a decarbonized residential future.

Configuration size optimization of gas-electric hybrid power systems on ships considering energy density and engine load response

System component sizes and their energy densities impact the long-term ship energy efficiency and economics, whereas power source load response influences system transient behavior, posing challenges to system design and energy management when clean fuels are used onboard. In this paper, a novel component sizing method is proposed for integrating reciprocating gas engines and energy storage systems (ESS) on ships, considering energy density and load response simultaneously. By specifying gas engine powers and ESS capacities as normalized optimization variables by ship scales, this method optimizes them based on the A. J. Klee multi-index evaluation and single-objective particle swarm optimization (PSO). Energy densities of engines and ESS were introduced to demonstrate the trade-off between the power-splitting and increased system mass and hull resistance. The engine load response was incorporated into the rule-based energy management strategy (EMS) to limit power ramps. This method was examined in the case study by taking four cruise ships as examples. It can be found that implementing the proposed evaluation-based optimization can improve cruise ship energy efficiency while maintaining reliability over the voyage. It is possible to reduce fuel consumption and carbon emissions by up to 17.1% and 14.8%, respectively. The minimum system reliability can be increased from 0.025 to 0.475 during transients. Furthermore, ship life-cycle costs can be reduced compared to the original design method, and the payback period for gas engines, ESS, and fuel tanks can be shortened to less than six years. Among the various cases, the definition of normalized optimization variables broadens the applicability of this method away from a specific ship, allowing it to be applied to ships of various tonnages and types.

Allocation and Sizing of DSTATCOM with Renewable Energy Systems and Load Uncertainty Using Enhanced Gray Wolf Optimization

Over the last decade, flexible alternating current transmission systems (FACTS) have been crucial in ensuring optimal power distribution within modern power systems. A vital component of FACTS devices is the distribution static compensator (DSTATCOM), which is essential for maintaining a reliable power supply. It is commonly used for reactive power compensation, voltage regulation, and harmonic reduction. Determining the appropriate size and placement of DSTATCOMs is vital to ensuring their efficiency. This study introduces the improved gray wolf optimizer (I-GWO), a refined version of the classical gray wolf optimization (GWO) method. The I-GWO incorporates a dimension learning-based hunting (DLH) strategy to preserve population diversity, balance exploration and exploitation, and prevent the premature convergence of classical GWO. In this research, the I-GWO was applied to determine the optimum allocation and sizing of the DSTATCOMs, considering system constraints, including those presented by the intermittent and stochastic nature of the load and renewable energy resources, specifically wind and solar energy. The suggested approach was successfully tested on 33-, 69-, and 85-bus distribution systems and then compared with existing studies. The results demonstrated the I-GWO-based approach’s superiority in terms of reducing power losses, improving voltage profiles, and enhancing voltage stability.

A Techno-Economic Comparison of a Stand-alone Hybrid System for a Household: A Case Study for the Royal Commission in Saudi Arabia

It’s exciting today to use renewable off-grid sources for driving applications in the home sector (micro-grids). The current tendency is to use renewable energy sources to increase building sector energy efficiency and protect the environment. The economic element and the expense of building these grids is the biggest barrier to these applications. How to best choose the ratings and mix of certain renewable sources based on the location of the house is one of the crucial ways to lower the cost and boost the reliability of these grids. An efficient techno-economical design for a standalone hybrid system is presented in this research. A solar energy using photovoltaic (PV), wind energy, energy storage, and diesel generator are all parts of the system. This design is carried out by a thorough analysis of the techno-economical design of a standalone system to supply the required energy load for a typical residential building for the Saudi Arabian cities of Yanbu and Jubail. Yanbu, which is at latitude 24.0097 degrees north, and Jubail, which is at latitude 27.0726 degrees north, are the two cities’ respective geographical positions. These two cities were chosen for the study of the impact of metrological influences on building performance based on total energy cost, load needs, and energy consumption of the differences in their geographic positions. To improve the performance of the suggested hybrid system, some restrictions were chosen, including Total Annual Cost (TAC) and Net Present Cost (NPC). Results show that the proposed stand-alone hybrid systems can be used to increase the economics and energy efficiency of existing buildings. For Yanbu and Jubail cities, respectively, the optimal integration of a diesel generator, PV array, wind turbine, and energy storage results in a minimum energy cost of roughly $0.483/kWh and $0.487/kWh.

A conditional value at risk based stochastic allocation of SOP in distribution networks

Soft open points (SOPs) have been noticed to enhance the operation efficiency of distribution networks due to their capability of exchanging power between feeders as well as reactive power compensation. The uncertainty of distributed renewable energy sources (DRESs) and load demands make its optimal allocation and active power flow settings difficult. Uncertainties should be modeled through appropriate techniques to give more reliable schemes for SOPs allocation and regulation. However, in the probabilistic environment, decreasing the risk of violations from objectives and constraints is also important to guarantee confident solutions.This paper finds the optimal location of an SOP and its power flow setpoints, considering the uncertainty of DRESs and load demands. The mentioned uncertainties are handled by the efficient K-medoids data clustering technique. In addition to considering the expected value of losses and voltage deviation as objective functions, the conditional value at risk (CVaR) of voltage deviation is considered, too. This ensures that in the final solution, the probability of voltage deviation in most worst scenarios will not be considerable. The proposed optimization model is implemented in a multi-objective optimization frame and solved by the multi-objective particle swarm optimization (MOPSO) algorithm and tested on the modified IEEE 33-bus test distribution system and the effect of considering the CVaR index on results is comprehensively discussed.

Machine and deep learning approaches for forecasting electricity price and energy load assessment on real datasets

This article aims to propose short-term forecasting for a combined model of the hourly electricity price and the hourly load. Where both the independent system operators and the market participants must deal with the uncertainty and volatility of the day-ahead electricity price. Therefore, an accurate prediction model has been needed for a combined short-term forecasting model for both the hourly electricity price and the hourly load in the deregulated market. To get a better prediction, real-time data for the day-ahead electricity price and the hourly load are provided from the ISO New England Control Area (ISO-NE-CA) market. This study presents the combined model based on four featured machine and deep learning algorithms: Feed Forward Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System, Long Short-Term Memory, and Gated Recurrent Units. The results are obtained from two scenarios, In the first scenario, the data has been trained and tested without depending on other factors such as temperatures and calendar. In the second scenario, the data has been trained and tested depending on the previously mentioned factors. The obtained results from the four algorithms have been compared to show their performance and their efficiency in the values of Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Normalized Root Mean Squared Error (NRMSE), and Mean Absolute Percentage Error (MAPE). The four featured algorithms prove their good performance with less error in the forecasting model.

Impacts of multiple demand-side management strategies on microgrids planning: a literature survey

Abstract Demand-side management (DSM) schemes play a crucial role in managing renewable energy generation and load fluctuations by utilizing demand–response programmes (DRPs). This paper aims to provide a detailed overview of DRPs that help microgrid operators to keep costs and reliability within acceptable ranges. Additionally, this review paper provides a detailed economic load model for DRPs based on initial load, demand–response (DR) incentive, DR penalty and elasticity coefficients. This article also aims to guide researchers in identifying research gaps in DSM applications in microgrids by comparing various DSM schemes from different countries and regions in terms of DSM strategies, objective functions and optimization techniques. Furthermore, this study analyses the impact of DRPs on microgrid configuration from the perspective of utilities and customers, considering technical and economic performance metrics. As a result, it can be concluded that none of the studied cases provides models or guidelines for choosing appropriate DSM schemes that consider different consumer interests or load-type features. Furthermore, a few researchers have addressed the features of a modern price-based DR strategy, renewable generation-based dynamic pricing DR, which offers higher customer satisfaction than traditional DRPs.

Electric Vehicle and Photovoltaic Power Scenario Generation under Extreme High-Temperature Weather

In recent years, with the intensification of global warming, extreme weather has become more frequent, intensifying the uncertainty of new energy output and load power, and seriously affecting the safe operation of power systems. Scene generation is an effective method to solve the uncertainty problem of stochastic planning of integrated systems of new energy generation. Therefore, this paper proposes a scenario generation and scenario reduction model of photovoltaic (PV) output and electric vehicle (EV) load power under extreme weather based on the copula function. Firstly, the non-parametric kernel density estimation method is used to fit a large number of sample data. The kernel density estimation expressions of PV and EV powers under extreme weather conditions are obtained and the corresponding goodness of fit tests are carried out. Then, a variety of joint distribution models based on the copula function are established to judge the goodness of fit of each model, and the optimal copula function is selected as the joint probability distribution function by combining the Kendall and Spearman correlation coefficients of each model. Finally, the optimal copula joint probability distribution is used to generate PV and EV power scenarios. The data of extremely hot weather in a certain province were selected for an example analysis. The results show that the output scenario obtained conforms to the correlation under this extreme weather, and has higher accuracy in reflecting the actual PV output and load power in this province under this extreme weather, which can provide a reference for reliability analyses of power systems and power grid planning.

Strategic Production and Trading in Renewable Energy Certificate Markets: Existence, Uniqueness, and Efficiency of Market Equilibria

As an incentive policy to promote renewable generation, the renewable standard portfolio (RPS) scheme has been widely adopted to enforce utility companies to procure a certain fraction of their energy supply from renewable sources. We construct a non-cooperative game theoretic model to study the strategic behavior of utility companies in a renewable energy certificate (REC) market, where utility companies purchase REC from renewable producers to meet their RPS obligation. In the constructed game, each utility company makes the renewable production decisions so as to minimize its total cost (the sum of renewable production and RPS penalty costs). We establish mild conditions that guarantee the existence of a Nash equilibrium. We further prove that if a Nash equilibrium exists, it must be unique and minimize the system cost. When symmetric utility companies' energy loads are independent and identical Gaussian distributed, as the number of utility companies increases to infinity, we show that the expected cost (of a utility company) converges to a lower bound, the product of the levelized cost of electricity (LCOE) of renewable generation and its expected RPS obligation. Numerical studies on real-world (load, RPS requirement, penalty, and LCOE) data show that the REC trading can reduce 5%-10% of the system cost.

Clinical outcomes of SMILE Xtra for myopia correction

This study assesses the treatment outcomes of patients with thin corneas and/or borderline corneal topography who underwent combined refractive surgery using the SMILE Xtra technique. The study included 53 patients (105 eyes) with myopia and a risk of developing keratectasia according to the Randleman criteria. All patients underwent prophylactic crosslinking following the SMILE procedure by introducing riboflavin solution into the corneal pocket, followed by ultraviolet irradiation according to the SUB-400 protocol with additional acceleration. The results were evaluated using data from Scheimpflug analyzer, analysis of corneal biomechanical properties, optical coherence tomography (OCT), and standard ophthalmological examinations. Follow-up lasted up to 12 months. The preoperative spherical equivalent of refraction was -4.09±1.30 D, and postoperative measurements on day 1, day 7, and at 1, 3, 6, and 12 months were: -0.08±0.64; 0.1±0.53; 0.2±0.48; 0.05±0.52; -0.29±0.47; 0.1±0.49 D, respectively. On day 1, uncorrected visual acuity (UCVA) increased to 0.78±0.17, rising to 0.94±0.11 by day 7, and remained stable throughout the follow-up. Mean keratometry values preoperatively were 44.32±1.3 D, and 40.89±1.65 D on day 1, with no statistically significant changes throughout the follow-up. Central corneal thickness decreased from 528.30±28.71 μm preoperatively to 459.32±28.92 μm on day 1 and 440.54±29.12 μm on day 7. Patient complaints of blurred vision on day 1 were associated with mild corneal edema due to residual riboflavin and energy load from UV treatment; these symptoms resolved by day 7. No regression of refractive results or keratectasia was observed throughout the follow-up period. SMILE Xtra can be used for myopia correction in cases with an increased risk of postoperative keratectasia in patients with thin corneas and/or borderline corneal topography readings.

Modeling the relationship between outdoor meteorological data and energy consumptions at heating and cooling periods: Application in a university building

<p style='text-indent:20px;'>In this study regression modeling is proposed for calculating the heating and cooling load of the buildings by considering the outdoor meteorological data. For this pupose, Balikesir University Rectorate Building is selected as the case building. In the winter months, measurements were made in the hot water boiler in the basement of the building for the heating energy load. For the cooling energy load, measurements were made in the chiller groups near the building. As climate data, eight variables were considered: outdoor temperature, solar radiation, relative humidity, wind speed, atmospheric pressure, sunshine duration, steam pressure, and 1 m underground temperature. The Minitab statistical analysis program was used to perform the modeling. 55 samples were used for mathematically modeling the heating load, while 37 samples are used for modeling the cooling load. The R<inline-formula><tex-math id="M1">\begin{document}$ {}^{2\ } $\end{document}</tex-math></inline-formula>(coefficient of determination) values are calculated as 96.2% and 98.94% for cooling load and heating load, respectively. In addition to these findings, ANOVA results for both models were examined and both models were found to be significant.</p>

Chance Constrained Distributed Optimisation for Interconnected Power Systems

Microgrids have been attracted increasingly attention due to renewable energy integration. Multiple microgrids are often interconnected for benefiting each other. A significant challenge in operating such power systems lies in the uncertainties including intermittence in renewable energy sources and load demands estimation. Sharing state variables in interconnected systems poses additional difficulties. This paper proposes a numerical solution approach to a chance constrained distributed optimisation for interconnected power systems under uncertainty. The inner-outer approximation approach is used for the solution and ALADIN for numerical implementation. An interconnected optimal power flow problem with a three-bus and a four-bus system is solved as a case study.

Enhancing Generation Expansion Planning With Integration of Variable Renewable Energy and Full-Year Hourly Multiple Load Levels Balance Constraints

Enhancing Generation Expansion Planning With Integration of Variable Renewable Energy and Full-Year Hourly Multiple Load Levels Balance Constraints

Intelligent ANFIS-Based Distributed Generators Energy Control and Power Dispatch of Grid- Connected Microgrids Integrated into Distribution Network

Power supply management is a critical problem in the operation of a distribution network, considering the causes of large power losses and interruptions in the main grid caused by the unknown connection of loads and DGs that affect power delivery to customers downstream of a distribution network. The above-mentioned problems can be reduced by the integration of microgrids close to load centers and developing a controller using adaptive control techniques to enhance reliable power supply. For this reason, this paper presents an intelligent method for distributed generators' energy control and power dispatch of microgrids integrated into a distribution network employing an Adaptive Neuro- Fuzzy Inference System (ANFIS). The aim is to control distributed generators energy sources, loads, and power dispatch of grid-connected microgrids among multiconnected power sources to maintain a stable power supply without using any optimization techniques. The proposed intelligent ANFIS system is trained for power-sharing purposes and applied to the microgrid controllers. The mathematical modeling of distributed generators, system design, simulation, and testing of the proposed method were done using MATLAB/Simulink software. The results show that the proposed controller is capable of power dispatch and controls the energy harvest of distributed generators. Additionally, it can assign microgrid power source(s) to additional load(s) connected to the active distribution network without interruptions of power flow. The obtained results outperform similar works that used hybrid ANFISPID (Adaptive Neural Fuzzy Inference System-Proportional- Integral-Derivative), PSO-ANFIS (Particle Swarm Optimization-Adaptive Neural Fuzzy Inference System), and GA-ANFIS (Genetic Algorithm-Adaptive Neuro-Fuzzy Inference System) by automatically connecting and controlling distributed generators energy sources with effective power dispatch in mitigating downtime of grid power operations.

An Operative IoT Grounded AEEBLR (Ant-Founded Efficient Energy and Balanced Load Routing) Method for Path Conjunction in Mobile Ad Hoc Networks Approach

An architecture for a wireless network that is constantly developing, decentralized, and multi-hop is called a mobile ad hoc network. MANETs are able to function in many different contexts where regular networks are unable. As can be seen from the advantages listed above, these networks are well-suited for a wide variety of applications, some of which include military and commercial use, as well as applications relating to disaster management, rescue operations, and defense. Energy conservation is a standard factor that indicates the overall network lifetime in mobile ad hoc networks that operate on rechargeable or replaceable battery. This is because usage, battery power consumption in relation to transmission range, type of application running on each device, location, and other influences all play a part in determining the overall network lifetime. An earlier study used a method called ant colony optimization, which is a form of swarm intelligence enthused by the activity of foraging ants in colonies. The best possible travel plan was found using this strategy. Current MANETS routing systems face difficulties in load balancing and energy efficiency that must be overcome if optimal path convergence is to be achieved. When deciding on the next hop node, the IoT based AEEBLR method is recommended. The latency, energy consumption, congestion, and connection quality are all taken into account before making a final decision. The likelihood of selecting the next-hop node as the neighbor node is determined using these metrics. It is the following hop's probability that determines which ant agent goes forward and which goes backward. This paves the door for the creation of many paths, from which the most effective might be chosen for transmission. The results of the implementation show that the suggested AEEBLR technique outperforms the existing AESR approach when the number of packets, the number of nodes, and the mobility of nodes are all varied.

PV integrated shipboard MVDC grid with reduced bus voltage ripple

Integration of renewable energy sources for power generation is a proven solution to reduce the increasing greenhouse gas emissions towards decarbonisation onboard ships. Also, it is easy to integrate a variety of energy sources in an all-electric ship under medium voltage direct current distribution system. In such distribution system, high frequency switching power converters are greatly involved on the source and the load sides, which are massive source of harmonics. In this paper, a grid connected photovoltaic module is designed and paralleled with the generator, as a step towards reducing greenhouse gas emissions. Additionally, to take care of the voltage ripple and noise created by the high frequency switching power converters in the system, a shunt direct current electric spring with a control strategy is incorporated. The proposed system eliminates the use of heavy passive filters, commonly used at the rectifier end. MATLAB/Simulink results substantiate that the increased voltage ripple with the inclusion of energy sources and loads, that goes around 10.91% under fully loaded condition, is reduced to 1.27% with the shunt direct current electric spring, hence well within the IEEE 1709 std., which is less than 5%.

Adaptive Time-Varying Routing for Energy Saving and Load Balancing in Wireless Body Area Networks

Routing plays an essential role in ensuring normal and lasting operation of wireless body area networks (WBANs). However, existing routing schemes cause inefficient and unbalanced energy dissipation, which contributes to premature death of some nodes and high temperature within a small area of the body. In this article, we propose an adaptive time-varying routing (ATVR) protocol to address these issues. Unlike in conventional routing solutions, in our protocol a node may act as different roles (source node or relay node) and select different paths in disparate periods. This dynamic routing pattern helps to achieve a globally optimal routing solution. In ATVR, a node evaluation function and a path evaluation function are designed to reflect the node state and the path state respectively. Then, the path selection problem is transformed into a Hitchcock transportation problem, in which the nodes with worse node state act as source nodes (i.e., producers) and the nodes with better node state act as relay nodes (i.e., consumers). Then, this Hitchcock transportation problem is addressed by the AlphaBeta algorithm, in which the paths with less energy consumption and less path loss are selected to forward data. The experimental results show that our protocol has better performance in terms of energy consumption, network lifetime, and node temperature.

Pull-Out Tests for GFRP/BFRP/Steel Bars Used as Nailing for Coal-Bearing Soil Slopes in Humid Regions

Abstract Coal-bearing soil slopes are associated with a high risk of landslides when subjected to high soil water content. Steel bars have been used as soil nailing for slope stabilization; however, corrosion may occur in an aggressive environment. Glass fiber reinforced polymer (GFRP) and basalt fiber reinforced polymer (BFRP) bars have higher resistance to corrosion and could be alternatives to steel bars, but their elastic modulus and bonding strength with cement concrete are inferior to steel bars, which may result in lower reinforcement effects against landslides and hence require further investigation. In this study, the mechanical properties of different types of bars were investigated using tensile tests. The mineral composition of the soil samples was analyzed. Subsequently, pull-out tests were conducted on three types of bars (steel, GFRP, and BFRP) embedded in grouts in the soil. Up to 38 test scenarios were investigated, and the results were statistically analyzed using an analysis of variance test. The effects of several factors were studied, including the bar type, water content, soil compaction degree, and soil surcharge. The results showed that the bar type had an insignificant effect on the maximum pull-out loads, indicating the feasibility of using GFRP and BFRP bars as alternatives to steel bars for soil nailing in coal-bearing soil slopes. The reinforcement effect can be weakened by rainfall or drought events and enhanced by higher compaction energy and surcharge loads.

Life-cycle assessment of microalgae liquid biofuel production in biofilm cultivation system via conversion technologies of transesterification, hydrothermal liquefaction and pyrolysis

Biofilm seems to be an ideal cultivation system for microalgae liquid biofuel conversion technologies due to the advantage of high biomass concentration. However, research on the biofilm cultivation system for microalgae liquid biofuel producing were limited. In this study, a life-cycle assessment was conducted for microalgae liquid biofuel production in this system via three conversion technologies: transesterification, hydrothermal liquefaction and pyrolysis. The result shows that scenario hydrothermal liquefaction seems to be the most feasible technology to produce liquid biofuel in biofilm system because only hydrothermal liquefaction achieved positive energy and environment benefits with a net energy ratio of 0.87 and Greenhouse gas emissions of −39.91 g CO2-eq MJ−1. For transesterification and pyrolysis, the net energy ratio (2.18 and 2.06) were larger than 1, while Greenhouse gas emissions (3.28 and 59.25 g CO2-eq) were greater than 0. This indicated that these scenarios exhibited negative energy and environment benefits due to heavy use of chemicals and high energy consumption during biomass drying process, respectively. Additionally, according to the sensitivity analysis, bio-crude mass yield exhibited a more significant impact on the energy and environment loads for scenario hydrothermal liquefaction than the other factors.

Day-Ahead P2P Energy Sharing Strategy Among Energy Hubs Considering Flexibility of Energy Storage and Loads

Day-Ahead P2P Energy Sharing Strategy Among Energy Hubs Considering Flexibility of Energy Storage and Loads