Terms Of Electricity Cost Research Articles

Solar energy‐based extraction of essential oils from cloves, cinnamon, orange, lemon, eucalyptus, and cardamom: A clean energy technology for green extraction

AbstractConventional essential oil extraction systems cannot meet the green extraction concepts and sustainable development goals (SDGs). Therefore, there is a need to develop alternative systems based on clean energy. The present study aims to investigate the possibility of essential oil extraction using solar energy without utilizing synthetic solvents. Hence, an eco‐friendly solar energy‐based extraction system (SEE) was developed to extract essential oils from cloves and cinnamon barks, orange and lemon peels, eucalyptus leaves, and cardamom seeds. Essential oils were assessed in terms of yield, physical properties, and chemical composition using gas chromatography–mass spectrometry assay. Also, the process sustainability was analyzed by the green extraction evaluation approach, and results were compared with those of traditional hydro‐distillation (THD). Replacing THD with SEE increased the extraction time by 0.65, 0.80, 0.41, 0.61, 0.39, and 0.45 h for processing clove, cinnamon, orange, lemon, eucalyptus, and cardamom, respectively. The optimum solar radiation for extraction was about 1000 W/m2. Also, oxygen compounds (96.45, 79.640, 42.98, and 83.76%) comprised the main proportion in the clove, cinnamon, eucalyptus, and cardamom essential oils, while orange and lemon essential oils had a high presence of terpenes. Furthermore, essential oils obtained by SEE had a similar yield and alcohol solubility to THD. According to green extraction process analysis, solar energy‐based extraction was 23–34% greener than THD, depending on the type of plant material.Practical ApplicationsThe innovative solar‐based extraction system designed and developed in this research can be used at farms and small factories to obtain essential oils using the zero‐energy concept. From the energy consumption viewpoint, this technology can be regarded as “free process” which can reduce production costs. Also, the unique design of the heating section of this new system prevents overheating of the sample, making this new solar‐based system suitable for the extraction of thermolabile compounds from plant materials. In this sense, obtaining a high‐quality essential oil without running cost (in terms of electricity cost), relatively small size, and low capital cost makes this system practical for future applications.

Smart Grid Energy Optimization and Scheduling Appliances Priority for Residential Buildings through Meta-Heuristic Hybrid Approaches

Smart grid technology has given users the ability to regulate their home energy use more efficiently and effectively. Home Energy Management (HEM) is a difficult undertaking in this regard, as it necessitates the optimal scheduling of smart appliances to reduce energy usage. In this research, we introduce a metaheuristic-based HEM system which incorporates Earth Worm Algorithm (EWA) and Harmony Search Algorithms (HSA). In addition, a hybridization based on the EWA and HSA operators is used to optimize energy consumption in terms of electricity cost and Peak-to-Average Ratio (PAR) reduction. Hybridization has been demonstrated to be beneficial in achieving many objectives at the same time. Extensive simulations in MATLAB were used to test the performance of the proposed hybrid technique. The simulations were run for multiple homes with multiple appliances, which were categorized according to the usage and nature of the appliance, taking advantage of appliance scheduling in terms of the time-varying retail pricing enabled by the smart grid two-way communication infrastructure algorithms EWA and HSA, along with a Real-Time Price scheme. These techniques helped us to find the best usage pattern for energy consumption to reduce electricity costs. These metaheuristic techniques efficiently reduced and shifted the load from peak hours to off-peak hours and reduced electricity costs. In comparison to HSA, the simulation results suggest that EWA performed better in terms of cost reduction. In comparison to EWA and HSA, HSA was more efficient in terms of PAR. However, the proposed hybrid approach EHSA gave the maximum reduction in cost which was 2.668%, 2.247%, and 2.535% in the case of 10, 30, and 50 homes, respectively, as compared to EWA and HSA.

Fuzzy compromised solution-based novel home appliances scheduling and demand response with optimal dispatch of distributed energy resources

Due to environmental issues and smart grid development, distributed energy resources, energy storage systems, and demand response (DR) are gaining attention to reduce the pollution and fossil fuel usage. This paper presents a customer’s preferences based innovative home appliances scheduling framework considering numerous constraints and DR for scheduling household appliances incorporating local energy grid and energy storage systems, including electrical and thermal energy storage. First, the models of household appliances and air conditioning load are built as the shiftable and non-schedulable loads and a flexible thermal load, respectively. Second, an enhanced normalized normal constraint (ENNC) strategy based on game theory (GT) is presented for a novel home appliance scheduling (HAS) framework with the objectives to optimize consumption cost, end-users comfort, and peak to average ratio. Then, the fuzzy compromising (FCP) method is proposed to optimize overall energy cost and gaseous emissions for the novel HAS framework with a residential local energy grid. In addition to this, conditional value at risk (CVaR) has also been incorporated in the objective function to resolve the sudden absence of distributed energy resources and power failures. Finally, case studies on data from Dallas, Texas, USA are performed, and the simulation results show that the proposed strategy is computationally inexpensive and outperforms other approaches in terms of electricity cost, gaseous emissions, and customer’s comfort. The proposed approach gives a significantly lower cost of 104.30 cents and gaseous emissions of about 18.753 kg for an entire day of novel HAS with DR adoption. Thus, it can provide help for DR accomplishment and precise prediction of electricity consumption.

Analysis on impact of shared energy storage in residential community: Individual versus shared energy storage

Considering a scenario where residential consumers are equipped with solar photovoltaic (PV) panels integrated with energy storage while shifting the portion of their electricity demand load in response to time-varying electricity price, i.e., demand response, this study is motivated to analyze the practical benefits of using shared energy storage in residential communities. The main objective of this study is to compare individual and shared energy storage operations economically (in terms of electricity cost) and operationally (in terms of energy storage use) with various parameter settings. We propose mathematical optimization models formulated to find optimal energy operations of individual and shared energy storage so that the best practices can be compared. Through the analysis of the optimal shared energy storage operations resulting from the mathematical optimization model, we intend to discover underlying patterns that can be used for developing efficient control policies tailored to shared energy storage use. We conduct numerical experiments using real historical data, and the results show that shared energy storage results in electricity cost saving with higher utilization compared to individual energy storage. Cost savings and energy storage utilization improvements up to 13.82% and 38.98%, respectively, exist when using shared energy storage instead of individual energy storage. We find that the maximum charging/discharging rate parameters have the most significant effect on individual and shared energy storage settings. We provide useful insights about how to assign residential consumers to shared energy storage and how to control changing and discharging shared energy storage by multiple consumers.

Optimal GWCSO-based home appliances scheduling for demand response considering end-users comfort

Nowadays, the most notable uncertainty for an electricity utility lies in the electrical demand and generation in power systems. Demand response (DR) accomplishment due to the home appliances energy management has acquired considerable attention for the reliable and cost-optimized power grid. The optimum schedule of home appliances is a challenging task due to uncertain electricity prices and consumption patterns. Given this background, an innovative home appliance scheduling (IHAS) framework is proposed based on the fusion of the grey wolf and crow search optimization (GWCSO) algorithm. Using the proposed technique, the cost of electricity reduction, users-comfort maximization, and peak to average ratio reduction is analyzed for home appliances in the presence of real-time price signals (RTPS). The proposed optimization algorithm is also employed for Air Conditioners (ACs) scheduling and end-users comfort maximization in its usage due to the high percentage of ACs load. Simulation results indicate that the proposed GWCSO approach is robust, computationally efficient, and outperforms conventional ones in terms of electricity cost, peak to average ratio, and it also demonstrate that there is a trade-off between users’ comfort considering appliances waiting time and electricity cost. Thus, it can provide guidance for precise electricity consumption predictions and different DR actions.

An Intelligent Integrated Approach for Efficient Demand Side Management With Forecaster and Advanced Metering Infrastructure Frameworks in Smart Grid

The development of advanced metering infrastructure (AMI) in smart grid (SG) had enabled consumers to participate in demand-side management (DSM) using the price-based demand response (DR) programs offered by the distribution companies (DISCO). This way, not only the consumers minimize their electricity bills and discomfort, but also the DISCOs can handle peak power demand and reduce the carbon (CO 2 ) emissions in a controlled manner. Building an optimization framework that will minimize cost, peak demand, waiting time, and CO 2 emission is not only a challenging task but also a concern of DSM. Most analyses are based on cost and peak-to-average ratio (PAR) minimization, but the effectiveness of the DSM framework is equally determined by user comfort and CO 2 emission. Considering only one objective (cost) or two objectives (cost and PAR) is not sufficient. Thus, for DSM framework to achieve these four relatively independent objectives at the same time, minimized cost, PAR, CO 2 emission, and user discomfort, an energy management controller (EMC) based on our proposed algorithm hybrid bacterial foraging and particle swarm optimization (HBFPSO) is employed that return optimal power usage schedule for consumers. A novel DSM framework consists of four units: (i) DISCO, (ii) multi-layer perceptron (MLP) based forecast engine, (iii) AMI, and (iv) demand-side energy management modules is successfully developed in this work. To validate the proposed model, extensive simulations are conducted and results are compared with the benchmark models like genetic algorithm (GA), bacterial foraging optimization algorithm (BFOA), binary particle swarm optimization (BPSO), and a hybrid combination of genetic and binary particle swarm optimization (GBPSO) in terms of electricity cost, PAR, user comfort, and CO 2 emissions. The simulation results demonstrate effectiveness of our proposed model to outperform all the benchmark models in optimizing the consumer and DISCO objectives. The proposed scheme has reduced electricity cost, user discomfort, PAR, and CO 2 emission for the residential sector by 15.14%, 4.6%, 61.6%, and 52.86% in scenario 1, 62.60%, 4.56%, 60.77%, and 27.77% in scenario 2, and 26.03%, 4.54%, 63.78%, and 23.02% in scenario 3, as compared to without an EMC. Similarly, for commercial sector the proposed HBFPSO algorithm reduces electricity cost, user discomfort, PAR, and CO 2 emission by 11.31%, 5.5%, 60.9%, and 38.18% in scenario 1, 64.9%, 5.56%, 44.08%, and 58.8% in scenario 2, 15.31%, 5.26%, 78.22%, and 15.58% in scenario 3. Likewise, the proposed algorithm also has superior performance for the industrial sector for all the three scenarios.

Hybrid meta-heuristic optimization based home energy management system in smart grid

The emergence of the smart grid has empowered the consumers to manage the home energy in an efficient and effective manner. In this regard, home energy management (HEM) is a challenging task that requires efficient scheduling of smart appliances to optimize energy consumption. In this paper, we proposed a meta-heuristic based HEM system (HEMS) by incorporating the enhanced differential evolution (EDE) and harmony search algorithm (HSA). Moreover, to optimize the energy consumption, a hybridization based on HSA and EDE operators is performed. Further, multiple knapsacks are used to ensure that the load demand for electricity consumers does not exceed a threshold during peak hours. To achieve multiple objectives at the same time, hybridization proved to be effective in terms of electricity cost and peak to average ratio (PAR) reduction. The performance of the proposed technique; harmony EDE (HEDE) is evaluated via extensive simulations in MATLAB. The simulations are performed for a residential complex of multiple homes with a variety of smart appliances. The simulation results show that EDE performs better in terms of cost reduction as compared to HSA. Whereas, in terms of PAR, HSA is proved to be more efficient as compared to EDE. However, the proposed scheme outperforms the existing meta-heuristic techniques (HSA and EDE) in terms of cost and PAR.

Day Ahead Real Time Pricing and Critical Peak Pricing Based Power Scheduling for Smart Homes with Different Duty Cycles

In this paper, we propose a demand side management (DSM) scheme in the residential area for electricity cost and peak to average ratio (PAR) alleviation with maximum users’ satisfaction. For this purpose, we implement state-of-the-art algorithms: enhanced differential evolution (EDE) and teacher learning-based optimization (TLBO). Furthermore, we propose a hybrid technique (HT) having the best features of both aforementioned algorithms. We consider a system model for single smart home as well as for a community (multiple homes) and each home consists of multiple appliances with different priorities. The priority is assigned (to each appliance) by electricity consumers and then the proposed scheme finds an optimal solution according to the assigned priorities. Day-ahead real time pricing (DA-RTP) and critical peak pricing (CPP) are used for electricity cost calculation. To validate our proposed scheme, simulations are carried out and results show that our proposed scheme efficiently achieves the aforementioned objectives. However, when we perform a comparison with existing schemes, HT outperforms other state-of-the-art schemes (TLBO and EDE) in terms of electricity cost and PAR reduction while minimizing the average waiting time.

Energy (in)justice in off-grid rural electrification policy: South Africa in focus

Generally speaking, increasing rural marginalisation in sub-Saharan Africa has sat alongside a rise in energy poor homes in rural off-grid communities. Even measures meant to improve electricity access have exacerbated the energy access gap between grid connected and off-grid homes. For example, the South African Non-Grid Electrification Policy Guidelines for electrifying off-grid, rural poor homes promote the adoption of Solar Home Systems (SHS), which are expected to produce 7.5 kWh/month on average. However, for poor homes within grid coverage, the Free Basic Electricity (FBE) programme allocates 50 kWh/month. This paper investigates the resulting disparity in terms of electricity cost (ZAR/kWh), including associated costs for heating, cooking and other needs. It does so through the energy justice framework, highlighting the mismatch in policy formulation (procedural injustice), resource distribution (distributive injustice) and spatial distribution (injustice in the recognition of population groups’ special needs). Through a combination of mathematics and social science perspectives, it then moves beyond a critique of the current SHS system to proposes a new one: a hybrid generation approach with a flexible pricing scheme and centralized system of operation that is both ethically compliant and capable of improving electricity access to off-grid communities with standards comparable to grid access.

Towards Efficient Energy Management and Power Trading in a Residential Area via Integrating a Grid-Connected Microgrid

Demand side management (DSM) is one of the most challenging areas in smart grids, which provides multiple opportunities for residents to minimize electricity cost. In this work, we propose a DSM scheme for electricity expenses and peak to average ratio (PAR) reduction using two well-known heuristic approaches: the cuckoo search algorithm (CSA) and strawberry algorithm (SA). In our proposed scheme, a smart home decides to buy or sell electricity from/to the commercial grid for minimizing electricity costs and PAR with earning maximization. It makes a decision on the basis of electricity prices, demand and generation from its own microgrid. The microgrid consists of a wind turbine and solar panel. Electricity generation from the solar panel and wind turbine is intermittent in nature. Therefore, an energy storage system (ESS) is also considered for stable and reliable power system operation. We test our proposed scheme on a set of different case studies. The simulation results affirm our proposed scheme in terms of electricity cost and PAR reduction with profit maximization. Furthermore, a comparative analysis is also performed to show the legitimacy and productiveness of CSA and SA.

Optimal Residential Load Scheduling Under Utility and Rooftop Photovoltaic Units

With the rapid advancement in technology, electrical energy consumption is increasing rapidly. Especially, in the residential sector, more than 80% of electrical energy is being consumed because of consumer negligence. This brings the challenging task of maintaining the balance between the demand and supply of electric power. In this paper, we focus on the problem of load balancing via load scheduling under utility and rooftop photovoltaic (PV) units to reduce electricity cost and peak to average ratio (PAR) in demand-side management. For this purpose, we adopted genetic algorithm (GA), binary particle swarm optimization (BPSO), wind-driven optimization (WDO), and our proposed genetic WDO (GWDO) algorithm, which is a hybrid of GA and WDO, to schedule the household load. For energy cost estimation, combined real-time pricing (RTP) and inclined block rate (IBR) were used. The proposed algorithm shifts load from peak consumption hours to off-peak hours based on combined pricing scheme and generation from rooftop PV units. Simulation results validate our proposed GWDO algorithm in terms of electricity cost and PAR reduction while considering all three scenarios which we have considered in this work: (1) load scheduling without renewable energy sources (RESs) and energy storage system (ESS), (2) load scheduling with RESs, and (3) load scheduling with RESs and ESS. Furthermore, our proposed scheme reduced electricity cost and PAR by 22.5% and 29.1% in scenario 1, 47.7% and 30% in scenario 2, and 49.2% and 35.4% in scenario 3, respectively, as compared to unscheduled electricity consumption.

Heating performance analysis of the region control method for heat pump with thermal storage system

A heat pump with thermal storage system comprises a heat pump and a thermal storage tank. Heat pumps store thermal energy in the thermal storage tank during the night; this then allows for the stored thermal energy and heat pump to meet the heating load demands during the day. This system contributes to the stabilization of the electric power supply and demand as well as makes it possible to realize the economic benefit of using inexpensive electric power during the night. Control strategies include the use of conventional control methods such as thermal storage priority and heat pump priority methods and region control methods. The region control method combines the advantages of both conventional methods. In this study, the system comprises a heat pump with a latent thermal storage tank. The heating load is that required for an office building and was calculated using the TRNSYS software. The power consumption and electricity cost were analyzed by conducting experiments according to the heating load size and each of the three control methods. The region control method was performed at the optimal operating ratio, which was the best in terms of electricity cost. It was noted that the utilization ratio of the thermal storage tank was also high. When comparing the region control method at 80 % design load with the thermal storage priority method, the heat pump coefficient of performance was higher and the power cost was lower by 5.7 %. However, the utilization ratio of thermal storage was higher than the heat pump priority method and the power cost was 5.2 % lower. Therefore, it is confirmed that the region control method is more effective than the conventional methods.

Energy efficiency aware load distribution and electricity cost volatility control for cloud service providers

This paper consider the case of a cloud service provider (CSP) who owns multiple geographically distributed data centers, with collocated sources of renewable energy. We investigate load distribution strategies to minimize electricity cost and increase renewable incorporation subject to compliance with service level agreement (SLA), considering the adverse effects of switching the servers. Our work provides some insights on the performance of different algorithms for geographical load balancing (GLB) in terms of electricity cost, renewable energy integration and number of server switching. Our proposed strategies incorporate a new way of capturing the server switching cost. We show that, instead of modeling switching cost through a linear function, the proposed technique of modeling switching cost through variance achieves a better tradeoff between some important parameters. Since the three major input parameters-electricity price, renewable energy and number of job requests-vary over time, the average cost of electricity per job request may also exhibit dramatic fluctuations. We propose to tackle this volatility by controlling the average cost of electricity per job request through leveraging contracts in the forward electricity market, and determine the optimal amount of electricity to be procured in the forward electricity market. We show that our proposed strategy substantially reduces the variance of the average cost of electricity per job and that this price risk mitigation is achieved with a decrease in the cumulative electricity cost.