Utilizing renewable energy sources efficiently in hospitals using demand dispatch

Nowadays companies can reduce their energy costs by using renewable energy sources, storage devices and optimized scheduling of charging/discharging of electric vehicles (EVs). This paper analyzes the influence of the number of EVs, photovoltaic production and daily variations of buying/selling prices on the reduction of energy costs. This problem is solved by power management and optimized scheduling of EVs charging/discharging, taking into account the intermittent behavior of renewable energy sources and vehicles demands for charging. Company savings are achieved by minimizing the costs of purchased energy from the main grid, by selling the energy from renewable energy sources and optimized scheduling of EVs charging/discharging. In this paper the costs are minimized by using optimization in Lingo.

The increasing demand for electricity and the inability of the current generation systems to meet the demand, urges for more renewable electrical energy systems in South Africa. The Rand West Municipality is a community that has a slow-paced rural electrification and is subject to multiple power cuts weekly thereby affecting the households, farms, and businesses in the area. It is therefore taken as a case study for the investigation of a Renewable Energy Sources (RES) microgrid. Simulation, optimization, and sensitivity analyses were conducted to determine the feasibility and optimal RES mix for the location using the Hybrid Optimization Model for Electric Renewable (HOMER) software. The simulations yielded an optimal microgrid system consisting of a combination of Solar Photovoltaic (PV) panels, Solar Concentrated Photovoltaic system, Battery Energy Storage System and a Diesel Generator with a Cost of Energy (COE) of $0.068/kWh, compared to the grid COE of $0.12/kWh for this area.

Renewable energy sources (RESs) offer a promising prospect for covering the fundamental needs of electricity for remote and isolated regions. To serve the customers with high power quality and reliability, design optimization methodology and a possible power management strategy (PMS) for wind-diesel-battery-converter hybrid renewable energy system (HRES) is proposed in this paper. The analysis is applied to a real case study of a standalone residential load located in a remote rural area in Pakistan. Firstly, optimal component sizing is investigated according to actual meteorological and load profile data. Different hybrid configurations are modeled, analyzed, and compared in terms of their technical, economic and environmental metrics with the aid of HOMER® software. The main objective is to determine the most feasible and cost-effective solution with least life-cycle cost, keeping in view the impact of carbon emissions. Secondly, a suitable PMS based on the state of charge (SOC) of the battery is proposed and implemented in MATLAB/Simulink® software for the designed HRES. The PMS is targeted to maintain load balance and extract maximum wind power while keeping the battery SOC within the safe range. Model predictive control (MPC) approach is applied to improve the output voltage profile and reduce the total harmonic distortion (THD). The boost converter is used for maximum power extraction from the wind. The DC-DC buck-boost battery controller is utilized to stabilize the DC bus voltage. The design optimization results show that the hybridization of wind, battery, and converter presents optimal configuration plan with minimum values of total net present cost (14,846 $) and cost of energy (0.309 $/kWh), which means 76.7% reduction in both total system cost and energy cost and 100% saving in harmful emissions when compared to the base case using diesel generator. The proposed system is able to support hundred percent of the load demand with excess energy of 30.1%. Performance analysis of PMS under variable load and fluctuating wind power generation is tested, and promising results with efficient load voltage profile is observed. Further, THD is reduced significantly to 0.26% as compared to 2.62% when the conventional PI controller is used. The output of this work is expected to open a new horizon for researchers, system planners for efficient design and utilization of HRES to curb drastic increase in load demand for urban as well as rural areas.

To tackle the challenge of improving Power Quality (PQ) in modern power grids, we introduce an innovative Internet of Things (IoT)-based Smart Grid (SG) energy surveillance system. Our research is driven by the necessity to enhance power quality and optimize energy management in increasingly complex grids that incorporate renewable energy sources like Solar PV and Wind Generating Systems. Traditional methods for managing power quality often fall short, resulting in inefficiencies and potential disruptions. Our solution features an advanced IoT-based system that utilizes the Adaptive Neuro-Fuzzy Inference System (ANFIS), combining Artificial Neural Networks (ANN) and Fuzzy Logic Systems to enhance power distribution and control. This system uses a Wireless Sensor Network for real-time data collection and analysis, allowing for precise monitoring of electricity usage and improved energy management and cost reduction. Our findings indicate that this innovative approach not only boosts power quality but also significantly enhances the efficiency of renewable energy sources, showing a 20.50% performance increase during the startup phase of Solar PV-Wind Generating Systems. This highlights the system’s potential to advance power quality management and provide substantial benefits in energy regulation and cost efficiency.

Demand Response (DR) programs represent an effective way to optimally manage building energy demand while increasing Renewable Energy Sources (RES) integration and grid reliability, helping the decarbonization of the electricity sector. To fully exploit such opportunities, buildings are required to become sources of energy flexibility, adapting their energy demand to meet specific grid requirements. However, in most cases, the energy flexibility of a single building is typically too small to be exploited in the flexibility market, highlighting the necessity to perform analysis at a multiple-building scale. This study explores the economic benefits associated with the implementation of a Reinforcement Learning (RL) control strategy for the participation in an incentive-based demand response program of a cluster of commercial buildings. To this purpose, optimized Rule-Based Control (RBC) strategies are compared with a RL controller. Moreover, a hybrid control strategy exploiting both RBC and RL is proposed. Results show that the RL algorithm outperforms the RBC in reducing the total energy cost, but it is less effective in fulfilling DR requirements. The hybrid controller achieves a reduction in energy consumption and energy costs by respectively 7% and 4% compared to a manually optimized RBC, while fulfilling DR constraints during incentive-based events.

The energy is substantial input for the economic growth of any country. In lieu of developing countries, increase in energy demand requires huge investments. Consequently, energy cost reduction, efficiency improvement and energy conservation utilization, management and audit are needed. In Sultanate of Oman, energy demand has been rising rapidly due to contemporary growing economic and population. The non-renewable natural gas produces energy. This prompts the move toward renewable energy sources and the implementation of Energy Efficiency and Conservation (EE&C) schemes such as energy audit and conservation. It is an analysis of the energy flow within a building that attempts to balance the energy input with its use not affecting the energy output. It is used to classify the entire energy stream. Energy management is used to attain and maintain an optimum energy flow to reduce energy costs and prevent the misuse of energy. Comprehensive EE&C measures are needed to conserve energy and for load leveling. In this paper, energy audit, conservation and management studies are conducted for Salalah College of Technology. The saving in the energy is achieved by implementing EE&C schemes through load observation, identifying the unnecessary usage of power and weak links in energy usage.

The Construction and Building Subcommittee of the National Science and Technology Council is developing baseline measures of current construction industry practices and measures of progress with respect to each of the seven National Construction Goals. The seven National Construction Goals are concerned with: (1) reductions in the delivery time of constructed facilities; (2) reductions in operations, maintenance, and energy costs; (3) increases in occupant productivity and comfort; (4) reductions in occupant-related illnesses and injuries; (5) reductions in waste and pollution; (6) increases in the durability and flexibility of constructed facilities; and (7) reductions in construction worker illnesses and injuries. This report provides a detailed set of baseline measures for National Construction Goal 2 (reductions in operations, maintenance, and energy costs) for educational facilities. As such, it describes data sources, data classifications, and the metrics used to develop the baseline measures. Extensive use of charts and tables is made throughout this document to illustrate the process by which the baseline measures were developed.

A smart energy management controller can improve energy efficiency, save energy costs, and reduce carbon emissions and energy consumption while accurately catering to consumer consumption habits. Having integrated various renewable energy systems (RESs) and a battery storage system (BSS), we proposed an optimization-based demand-side management (DSM) scheduler and energy management controller (SEMC) for a smart home. The suggested SEMC creates a DSM-based operational plan regarding user-centered and comfort-aware preferences. Using the generated appliances operation plan, consumers can reduce energy costs, carbon emissions, peak-to-average ratio (PAR), improve their comfort in terms of thermal, illumination, and appliances usage preferences. A schedule for residential consumers is suggested using ant colony optimization (ACO), teaching learning-based optimization (TLBO), Jaya algorithm, rainfall algorithm, firefly algorithm, and our hybrid ACO and TLBO optimization (ACTLBO) algorithm. Five existing algorithms-based frameworks validate the DSM framework that relies on ACTLBO. The results validate that the integration of RESs and BSS, and adapting our proposed algorithm and SEMC under demand response program real-time price reduced the energy bill costs, PAR and CO2 in Case I: only external grid (EG) usage by 42.14%, 22.05%, and 28.33%, in Case II: EG with RESs by 21.79%, 11.27%, 17.02%, and in Case III: EG with RESs and BSS by 28.76%, 41.53%, 21.86%, respectively as compared to without employing SEMC. Moreover, the user comfort improvement index-ratio with scheduling using ACTLBO is 7.77%, 24.73%, 5.00%, and 3.43% in terms of average delay, indoor air quality, thermal, and visual, respectively. Simulation results show that the proposed DSM-based framework outperforms existing frameworks to reduce energy bill costs, reduce carbon emissions, mitigate peak loads, and improve user comfort.

The primary objective of this study is to assess potential savings opportunities in agricultural enterprises and to determine their feasibility. To this end, face-to-face interviews were conducted with 268 agricultural enterprises in Turkey, selected according to the stratified random sampling method. The results of the interviews revealed the amount of savings accrued by agricultural enterprises and the factors influencing the formation of savings. The data obtained was then used to determine the savings potential of agricultural enterprises using artificial neural networks. The classification analysis in the artificial neural network model enables the prediction of the savings potential of enterprises by classifying them according to all the variables included in the model, in comparison to the classification made with the existing data. This approach allows for the consideration of not only financial indicators but also socio-economic factors, personal factors and environmental factors in determining savings policies, thereby revealing the actual potential of the enterprises. To determine the savings potential of the analyzed enterprises, 29 different models in 9 model classes were tested. Consequently, the model class with the highest accuracy was identified as decision trees. The accuracy of decision trees varies between 82.5% and 85.1%. While 62.69% of the enterprises exhibited high savings because of the modelling process, this value was determined as 60.8% because of the prediction model. Furthermore, the proportion of enterprises with low savings, which was estimated at 32.84% in the data model, was found to be 35.8% in the prediction model. Additionally, the proportion of enterprises with negative savings was determined to be 4.48% in the data model and 3.4% in the prediction model. The study identified the structural, social and economic characteristics of enterprises according to their savings structures and evaluated potential for increasing savings. It was determined that agricultural enterprises should focus on ways to increase savings, increase income, keep expenses under control and make investments for the future. Efficient farming techniques, the formation of agricultural cooperatives and marketing associations, the reduction of energy and input costs, the effective utilization of agricultural machinery, and the investment in renewable energy sources could assist agribusinesses in increasing their economic security and sustainability. All these steps enable agricultural households to have a stronger and more sustainable financial structure and contribute to the economic development of rural areas.

Renewable energy systems are becoming more and more common as people turn away from conventional energy sources. Microgrids are incorporated with renewable energy sources as they guarantee an economically and environmentally sound solution. The main problem arises when electric vehicle charging stations are integrated with microgrids. It would be required for the microgrid to have a synchronized and optimal charging system in order to accommodate several EVs. Using the HOMER Grid software, various configurations are simulated in this paper. Various renewable energy sources, tariff plans, and size of the components are used to create the configuration to determine the system that performs the best economically and environmentally. With regard to factors such as annual energy costs, cost reductions, and carbon emissions, among other things, a comparison of the current system with the optimized system is performed. As a result, the NPC, O&M and LCOE of the system decreased by ₹ 2.56M, ₹ 4.52L, and ₹ 4.03 respectively and the carbon emissions got reduced significantly.

Sustainable thermal-based desalination with low-cost energy resources and low-carbon footprints

Wind farms are generally designed with turbines of all the same hub height. If wind farms were designed with turbines of different hub heights, wake interference between turbines could be reduced, lowering the cost of energy (COE). This paper demonstrates a method to optimize onshore wind farms with two different hub heights using exact, analytic gradients. Gradient‐based optimization with exact gradients scales well with large problems and is preferable in this application over gradient‐free methods. Our model consisted of the following: a version of the FLOw Redirection and Induction in Steady‐State wake model that accommodated three‐dimensional wakes and calculated annual energy production, a wind farm cost model, and a tower structural model, which provided constraints during optimization. Structural constraints were important to keep tower heights realistic and account for additional mass required from taller towers and higher wind speeds. We optimized several wind farms with tower height, diameter, and shell thickness as coupled design variables. Our results indicate that wind farms with small rotors, low wind shear, and closely spaced turbines can benefit from having two different hub heights. A nine‐by‐nine grid wind farm with 70‐meter rotor diameters and a wind shear exponent of 0.08 realized a 4.9% reduction in COE by using two different tower sizes. If the turbine spacing was reduced to 3 diameters, the reduction in COE decreased further to 11.2%. Allowing for more than two different turbine heights is only slightly more beneficial than two heights and is likely not worth the added complexity.

Integrating labor awareness to energy-efficient production scheduling under real-time electricity pricing: An empirical study

Optimal energy management of a grid-connected dual-tracking photovoltaic system with battery storage: Case of a microbrewery under demand response

Renewable energy (RE) microgrids are considered one solution for solving the increasing electricity demand and environmental pollution problem. Selecting RE sources for a microgrid plays a crucial role in determining the performance of the microgrid. Illustrated with an empirical study on a city's microgrid project, this article proposes a new evaluation approach with a bi-capacity based multi-criteria decision making method called Bi-ELECTRE to evaluate and select RE source alternatives based on interacting performance indicators with bipolar measurement. With the novelty of the Bi-ELECTRE method, this article addresses the questions of (a) how to measure interactions between bipolar-scaled indicators when evaluating the RE source alternatives of a microgrid, and (b) how to determine the optimal combination of RE sources for a microgrid. The results from the empirical study suggest that a microgrid with a single RE source has the worst performance, compared with multi-source alternatives. In the empirical study conducted, the microgrid using a RE source combination of 20% wind, 30% solar, and 50% biomass has the best overall performance. By considering interactions between bipolar-scaled indicators and various combinations of RE sources, the proposed approach contributes to MCDM research methodologically and to the RE source selection problem for a microgrid practically.