Electricity Cost Savings Research Articles

Comparative analysis of operational indicators of air separation plants

The article gives tips on how to achieve real savings in electricity costs during the operation of an oxygen station, determined by the speed of performance regulation and the launch of cryogenic and adsorption technologies. A comparative analysis of obtaining 40,000 Nm³ /hr of oxygen with a purity of at least 93% and a pressure of 0.5 MPa at the output of the oxygen station with round-the-clock and year-round operation was carried out. The optimal solution to the problem under consideration is the use of cryogenic technology. The advantage of adsorption technology, compared to cryogenic one, is the ability to quickly stop and enter the mode. However, this advantage can manifest itself only in cases when there is no constant oxygen consumption at the enterprise, the periods of equipment downtime are long and unpredictable, the power supply system is unstable, and the required peak costs are very significant, which does not allow the use of a liquid product storage park. The practical value of the article is that the customer's service can determine how much savings will be comparable to the excess of the present cost of capital and operating costs for adsorption technologies, ranging from $ 25 million (without adsorbent replacement) to $ 40 million (with adsorbent replacement) over 20 years.

Optimal Energy Scheduling Based on Jaya Algorithm for Integration of Vehicle-to-Home and Energy Storage System with Photovoltaic Generation in Smart Home.

With the emerging of the smart grid, it has become easier for consumers to control their consumption. The efficient use of the integration of renewable energy sources with electric vehicle (EV) and energy storage systems (ESSs) in the smart home is a popular choice to reduce electricity costs and improve the stability of the grid. Therefore, this study presents optimal energy management based on the Jaya algorithm for controlling energy flow in the smart home that contains photovoltaic generation (PV), integrated with ESS and EV. The objective of the proposed energy management is to reduce electricity cost while meeting the household load demand and energy requirement for the EV trip distance. By using the Jaya algorithm, the modes of home-to-vehicle (H2V) and vehicle-to-home (V2H) are controlled, in addition to controlling the purchase of energy from the grid and sale of the energy to the grid from surplus PV generation and ESS. Before EV participation in the V2H process, the amount of energy stored in the electric vehicle battery will be verified to be more than the energy amount required for the remaining EV trip to ensure that the required energy for the remaining EV trip is satisfied. Simulation results highlight the performance of the optimal energy scheduling to achieve the reduction of the daily electricity cost and meeting of load demand and EV energy required. The simulation results prove that optimal energy management solutions can be found with significant electricity cost savings. In addition, Jaya is compared with the particle swarm optimization (PSO) algorithm in order to evaluate its performance. Jaya outperforms PSO in terms of achieving optimal energy management objectives.

Effects of power tariffs and aeration dynamics on the expansion of water resource recovery facilities

Water resource recovery facilities are major points of electricity demand and exhibit dynamic electricity demand due to their influent load as well as their diurnal and seasonal cycles. This dynamic demand along with the diurnal and seasonal nature of grid electricity cost as well as greenhouse gas emission intensity (i.e., emission produced to generate unit grid electricity) together demonstrate the importance of modelling these facilities dynamically to accurately assess their energy demand, operating cost, extent of removal, and greenhouse gas emissions. This article reports the results of a dynamic modelling effort that examines the effect of an expansion project on the extent of treatment, electricity demand, carbon footprint, and the operating cost of a large water resource recovery facility in California. This expansion consists of the addition of a differently configured activated sludge unit and associated secondary clarifiers to this plant's existing unit. The presence of these two parallel activated sludge configurations in the expanded plant was expected to provide an opportunity to achieve electricity demand and cost savings especially during the facility's coinciding peak of electricity demand with the grid's demand by maximizing the use of more energy efficient configuration. This electricity saving approach is an example of a grid demand-side flexibility project. However, the observed inverse correlation of the facility electricity demand and its cost with the overall operating cost and extent of treatment after the addition of the second activated configuration, which was determined to be the less energy efficient of these two configurations, demonstrated the infeasibility of such a grid demand-side flexibility project. This inverse correlation is changed to a positive correlation if the electricity efficiency of the added activated sludge configuration as part of this expansion project exceeds the existing electricity efficiency. As a result, recommendations were made accordingly for future studies to improve this configuration's electricity efficiency and to make the proposed grid demand-side flexibility project feasible. Such electricity efficiency improvement is also expected to lower the plant's overall carbon footprint which was not significantly impacted as part of this expansion project.

Flexible operation of modular electrochemical CO2 reduction processes

Electrochemical CO2 reduction (eCO2R) is an emerging technology that is capable of producing various organic chemicals from CO2, but its high electricity cost is a big economic obstacle. One solution to reduce the cumulative electricity cost is demand side management, i.e., to adjust the power load based on time-variant electricity prices. However, varying the power load of CO2-electrolyzers often leads to changes in Faraday efficiency towards target components and thereby influences the product composition. Such deviations from the target product composition may be undesired for downstream processes. We tackle this challenge by proposing a flexible operating scheme for a modular eCO2R process. We formulate the economically optimal operation of an eCO2R process with multiple electrolyzer stacks as a parallel-machine scheduling problem. Adjusting the power load of each sub-process properly, we can save electricity costs while the desired product composition is met at any time. We apply an algorithm based on wavelet transform to solve the resulting large-scale nonlinear scheduling problem in tractable time. We solve each optimization problem with a deterministic global optimization software MAiNGO. We examine flexible operation of a modular eCO2R process for syngas production. The case studies show that the modular structure enables savings in the cumulative electricity cost of the eCO2R process via flexible operation while deviations in the syngas composition could be reduced. Also, the maximum ramping speed of the entire process is found to be a key parameter that strongly influences the cost saving.

Analysis of financial and carbon savings of grid-tied home energy systems in conjunction with photo-voltaic solar generation and electric vehicle use

The general downward trend of the costs of both battery and solar technologies has helped lead to their increased adoption on both utility and residential levels. These decreasing costs combined with the relative ubiquity of solar power presents a significant opportunity for homeowners to not only reduce their overall electric bill but their carbon footprint by pairing solar generation with home energy systems (HES). By incorporating HES, consumers are able “peak shave” to take advantage of favorable Time-of-Use electricity rates as well as capture excess power generated by solar panels that would otherwise be sent back to the grid as part of a net metering agreement or sold back at the “avoided cost” rate, which is often times unfavorable. Depending on the scale of said system, the consumer can significantly reduce their carbon footprint, or in some cases offset it entirely, even more so if they use an electric vehicle (EV). These carbon savings, in the presence of incentives, can also provide greater financial benefit in addition to the electricity cost savings. The overall cost and CO2 savings associated with installing such a system is a function of a variety of factors such as the solar availability, house load, incentives, rates, and the grid mix (coal, wind, nuclear, etc). All of these factors are regional; thus analyses of these systems must be done on a regional basis. In this paper, we model these systems using data from several regions across the United States and quantity the financial viability of these systems as well as their environmental benefit as measured by CO2 emissions reductions. As will be shown, the benefits differ depending on region as well as the time-of-use rate schemes deployed by different utility companies. It is hoped that yielding favorable results in both of these metrics will accelerate adoption of these systems and assist in the mitigation of climate change.

Techno-economic Analysis of Rooftop Photovoltaic System (RPVS) using Thin-Frameless Solar Panels for Household Customers in Indonesia

The availability of thin-frameless solar panels on the market today makes the installation of rooftop Photovoltaic (RPVS) systems more attractive. The purpose of this research is to analyze financially the use of thinframeless solar panels for on-grid RPVS by household electricity customers in Indonesia. The investment cost, the maintenance costs, and the electricity cost savings were involved for the financial analysis, such as Internal Rate of Return (IRR), Net Present Value (NPV), and Pay Back Period (PBP). The calculation is carried out for ideal conditions, the direction of a non-ideal rooftop and the yearly increase of electricity prices is 15 %. The analysis results show that the minimum available rooftop area is still sufficient for the rooftop area needs for solar panel placement, the thin solar panels are safer than standard solar panels, and savings on electricity payments for the return on investment of the RPVS is to be attractive with the IRR > 12 %. The average investment cost of the non-ideal condition is 8 % higher than the ideal condition. This study provides an overview to the policymakers and developers in exploiting the potential of RPVS, especially in Indonesia. For future research directions, this study needs to analyze the technical and economic feasibility of using hybrid smart-grid technology with batteries.

Consumer-focused solar-grid net zero energy buildings: A multi-objective weighted sum optimization and application for India

Abstract Buildings account for nearly 40% of the world's energy consumption. Renewable energy sources can help decarbonization by facilitating the achievement of Net Zero Energy Buildings (NZEB). NZEB can be achieved by efficient coordination of the grid-connected load in the building tuned to a consumer's energy demand, usage pattern, and available energy sources. An effective NZEB system calls for an energy-efficient management technique. This paper proposes a novel mathematical strategy for electrical energy management in a residential building. The suggested method establishes a Net Zero Energy (NZE) in a residential building with maximum electrical energy comfort and minimum drain on electrical energy. We validate the concept using a Multi-Objective Differential Evolution optimization methodology using a consumer-controlled approach to obtain Pareto points to guarantee optimal solutions in an NZEB. The proposed scheme delineates the net cumulative electrical energy cost savings of the consumer, ensuring minimum electrical energy discomfort, and minimization of carbon footprints in an NZE Photovoltaic-Grid tied residential building. We highlight the socioeconomic benefits of adopting the methodology in identifying the true potential of NZEBs in meeting global challenges. This research provides insights to the engineers and policy makers to increase consumer participation in the energy performance in an NZEB.

Impact of the Pumping Regime on Electricity Cost Savings in Urban Water Supply System

The main purpose of the presented research is to raise the efficiency of pumping stations’ operational work by developing a model for reducing energy costs in urban water supply systems. Pumping systems are responsible for a significant portion of the total electrical energy use. Significant opportunities exist to reduce the pumping energy through smart design, retrofitting, and operating practices. Today, considering the increase in pumping energy prices in water conveyance systems, the problem of optimal operation of pumping stations is very actual. The optimal operation of pumping stations was determined using a Genetic Algorithm Optimization (GAO) to achieve the minimum energy cost. The paper presents a novel management model for the optimal design and operation of water pumping systems on a real case study for the town of Gonabad in Iran. To achieve this goal, three days in a year were selected randomly. The results indicate that the proposed mode in conjunction with a GAO is a versatile management model for the design and operation of the real pumping station. Modeling results show that optimization with a GAO reduces power consumption by about 15–20%.

Photovoltaic self consumption analysis in a European low voltage feeder

Electricity self-consumption consists in consumers installing their own small generators, solar photovoltaics mainly. They attempt to save money in the electric bill by producing part of their electrical energy, consequently reducing dependency on the distribution grid. Consumers that simultaneously become producers are usually known as prosumers. This work shows the techno-economical implications, in terms of electric energy, cost savings and grid voltage impact, if self-consumption is deployed in a European low voltage feeder. In the case study, the IEEE European LV test feeder was used. Real data for solar irradiance from 1985 to 2017 were considered for Gijón, a city in the North of Spain. Conclusions may be extrapolated to different locations.

Impacts of avalanche effects of price-optimized electric vehicle charging - Does demand response make it worse?

Electric vehicles (EVs) are expected to provide substantial potential for demand response (DR) and, thus, the integration of renewable electricity sources in the future energy system. However, DR can also have noticeable negative consequences, so-called avalanche effects. We systematically assess under which circumstances avalanche effects occur and what impact they have on the electricity system and cost savings for EV owners. Our results show that DR can provoke unwanted avalanche effects, which are particularly strong beyond 2030, when the leverage of EV charging will have increased to a significant level. It is possible to avoid avalanche effects by using a dynamically updated DR signal. If this is used, our findings confirm that shifting charging load from peaks to hours of low or negative residual load reduces the peak and variance of residual load and facilitates the integration of renewables.

Study of the biogas potential generated from residue: peanut shells

The increase in agricultural production generates a large volume of waste, which may lead to concerns about its proper destination. The main economic activity in Herculândia City, Western region of São Paulo State, Brazil, is the production and processing of peanuts. In this process, a large volume of peanut shells is generated. Following the current movement of using waste for energy purposes, in compliance with what was established by the Sustainable Development Goals (SDGs), this work aimed to carry out a study on the biogas potential generated from peanut shells. To this end, a low-cost biodigester prototype was built, which, over a period of 108 days, produced biogas and biofertilizer. The results showed that there was production of biogas from peanut waste; however, the volume produced did not provide savings in electricity costs when compared to the production of biogas from animal waste. Nevertheless, the work demonstrated the importance of providing solutions to the disposal of peanut shells, effectively mitigating future environmental problems, and serving as an alternative for generating sustainable and low-cost energy, especially for small producers.

Deep Learning in Energy Modeling: Application in Smart Buildings With Distributed Energy Generation

Buildings are responsible for 33% of final energy consumption, and 40% of direct and indirect CO2 emissions globally. While energy consumption is steadily rising globally, managing building energy utilization by on-site renewable energy generation can help responding to this demand. This paper proposes a deep learning method based on a discrete wavelet transformation and long short-term memory method (DWT-LSTM) and a scheduling framework for the integrated modelling and management of energy demand and supply for buildings. This method analyzes several factors including electricity price, uncertainty in climatic factors, availability of renewable energy sources (wind and solar), energy consumption patterns in buildings, and the non-linear relationships between these parameters on hourly, daily, weekly and monthly intervals. The method enables monitoring and controlling renewable energy generation, the share of energy imports from the grid, employment of saving strategy based on the user priority list, and energy storage management to minimize the reliance on the grid and electricity cost, especially during the peak hours. The results demonstrate that the proposed method can forecast building energy demand and energy supply with a high level of accuracy, showing a 3.63-8.57% error range in hourly data prediction for one month ahead. The combination of the deep learning forecasting, energy storage, and scheduling algorithm enables reducing annual energy import from the grid by 84%, which offers electricity cost savings by 87%. Finally, two smart active buildings configurations are financially analyzed for the next thirty years. Based on the results, the proposed smart building with solar Photo-Voltaic (PV), wind turbine, inverter, and 40.5 kWh energy storage has a financial breakeven point after 9 years with wind turbine and 8 years without it. This implies that implementing wind turbines in the proposed building is not financially beneficial.

Elimination of the juice clarifier and the drum vacuum filter at a cane sugar plant

The sugar manufacturing process includes many stations with specific residence times for proper and complete reactions. Sugar losses due to inversion in sugar processing are mainly influenced by temperature, pH value, d.s. content, and residence time of the juices/syrups. Reducing the residence time by the elimination of some process steps would reduce sucrose loss. Recycling of clarifier underflow into the cane diffuser was initiated in 2004. At the sugar units of The Andhra Sugars Limited the drum vacuum filter is no longer used. In the 2012/13 milling season at plant-3 a plant-scale run of clarification of juice in the diffuser to produce diffuser clear juice (DCJ) was carried out. The juice clarifier was bypassed with encouraging process and financial outcomes. With the elimination of the drum vacuum filter and the juice clarifier, there was a gain in sugar recovery as well as savings in manpower, electricity, steam, and maintenance costs. For a new raw sugar plant using this process the capital costs would be reduced.

Building energy management decision-making in the real world: A comparative study of HVAC cooling strategies

In recent years, various heating, ventilation, and air-conditioning (HVAC) control strategies have been proposed to reduce buildings’ energy consumption and environmental impacts. Due to different research designs, it is impossible to directly compare the reported performance of these strategies and determine the best approach for operating real-world buildings. To fill this gap, this research uses a well-validated building simulation model to holistically evaluate the performance of 12 existing HVAC cooling strategies in reducing the total cooling energy use, peak demand, and cooling energy cost. The findings include: i) The ON/OFF and night setup control strategies could lead to even higher energy cost than the 24/7 operation baseline; ii) demand-limiting control methods performed well across the three energy performance metrics and achieved the highest energy reduction ratios ranging from 9.8% to 10.5%; iii) precooling and extended precooling reduced the peak load most significantly by 15.4% and 21.4%, respectively; and iv) the resistive-capacitive (RC) network-based precooling optimization resulted in the highest electricity cost savings with reduction ratios of 16.6% and 12.9%, based on the two common price schedules. Besides providing valuable insights to the research community, this study offers a practical method to help building operators analyze and select the best HVAC control strategies for their energy management goals.

Demand response through automated air conditioning in commercial buildings\u2014a data-driven approach

Building operation faces great challenges in electricity cost control as prices on electricity markets become increasingly volatile. Simultaneously, building operators could nowadays be empowered with information and communication technology that dynamically integrates relevant information sources, predicts future electricity prices and demand, and uses smart control to enable electricity cost savings. In particular, data-driven decision support systems would allow the utilization of temporal flexibilities in electricity consumption by shifting load to times of lower electricity prices. To contribute to this development, we propose a simple, general, and forward-looking demand response (DR) approach that can be part of future data-driven decision support systems in the domain of building electricity management. For the special use case of building air conditioning systems, our DR approach decides in periodic increments whether to exercise air conditioning in regard to future electricity prices and demand. The decision is made based on an ex-ante estimation by comparing the total expected electricity costs for all possible activation periods. For the prediction of future electricity prices, we draw on existing work and refine a prediction method for our purpose. To determine future electricity demand, we analyze historical data and derive data-driven dependencies. We embed the DR approach into a four-step framework and demonstrate its validity, utility and quality within an evaluation using real-world data from two public buildings in the US. Thereby, we address a real-world business case and find significant cost savings potential when using our DR approach.

Optimal design of an integrated renewable‐storage energy system in a mixed‐use building

The rise of mixed-use buildings contributes to the sustainable development of cities but are still met with challenges in energy management due to the lack of energy efficiency and sustainability guidelines. The use of integrated renewable-storage energy systems is a more beneficial solution to this problem over individual solutions; however, most design studies only focused on single-type buildings. Thus, this study aims to optimally design an integrated energy system for mixed-use buildings using HOMER Grid. The objective is to minimize the net present costs, subject to capacity limits, energy balances, and operational constraints. Economic metrics were used to evaluate and compare the proposed system to the varying design cases such as business-as-usual, stand-alone renewable source, and stand-alone energy storage. The case study considered a mixed-use building in a tropical area, with a solar photovoltaic system as the renewable energy source and lithium-ion battery as the energy storage system technology. The results show that the integrated system is the most financially attractive design case. It has a levelized cost of electricity of 0.1384 US$ kWh−1, which is significantly less than the 0.2580 US$ kWh−1 baseline. The system also provides electricity cost savings of 294 698 US$ y−1, excess electricity of 35 746 kWh, and carbon emission reduction of 550 tons annually for a mixed-use building with daily average consumption of 4557-kWh and 763-kW peak demand.

Optimal use of mobile cooling units in a deep-level gold mine

The South African gold mining sector remains a significant contributor to the country’s economy. Facing several challenges that hinder the realisation of South Africa’s full mineral potential, the sector’s sustainability and profitability can be enhanced through implementing operational improvement measures. Mobile cooling units (MCUs) were identified as a potential focus area for operational improvement. MCUs are used as tertiary or in-stope cooling in hot underground workings. In this paper, a method was presented to characterise the performance of existing MCUs based on three key performance indicators (KPIs), namely, the wet-bulb temperature ratio (WTR), efficiency and position. Optimisation strategies were then elected and implemented based on these KPIs. The implementation of this method in a South African gold mine attained a reduction in pumped water volumes, reduced operating costs through electricity cost savings and improvements in underground ventilation air temperatures.

Multi-stage stochastic programming for demand response optimization

The increase in the energy consumption puts pressure on natural resources and environment and results in a rise in the price of energy. This motivates residents to schedule their energy consumption through demand response mechanism. We propose a multi-stage stochastic programming model to schedule different kinds of electrical appliances under uncertain weather conditions and availability of renewable energy. We incorporate appliances with chargeable and dischargeable batteries to better utilize the renewable energy sources. Our aim is to minimize the electricity cost and the residents’ dissatisfaction. We use a scenario groupwise decomposition (group subproblem) approach to compute lower and upper bounds for instances with a large number of scenarios. The results of our computational experiments show that the approach is very effective in finding high quality solutions in small computation times. We provide insights about how optimization and renewable energy combined with batteries for storage result in peak demand reduction, savings in electricity cost and more pleasant schedules for residents with different levels of price sensitivity.

COMPARISON OF ENERGY CONSUMPTION OF VARIOUS ELECTRICAL MOTORS OPERATING IN A PUMPING UNIT

Purpose. Comparative analysis of energy consumption of various types electric motors in fixed speed centrifugal industrial pump applications is carried out. The purpose of the analysis is to choose the most efficient motor in the considered application. It is assumed that hydraulic flow of the pump is adjusted by throttling. The rated power of the pump unit is 2.2 kW. Direct on line motors of various energy efficiency classes from various manufacturers are considered: induction motors with permanent magnets on the rotor of IE4 class and squirrel cage induction motors of IE3 and IE4 classes. Methodology. Assessment of energy consumption of the motors is carried out based on the catalogue data from manufacturers of the pump and the motors. Pump hydraulic equations, interpolation of motor catalogue data and statistical data are also used. Results. The following values have been obtained: annual and daily energy consumption of the motors and electricity cost savings comparing with the least effective motor considered. Practical value. The following practical consideration are presented based on the theoretical results: choosing the motor based only on its IE efficiency class according to IEC 60034-30-1 is not enough to ensure the minimum energy consumption of pump units with variable flow during the load cycle. In addition, the energy consumption may be higher in the case of permanent magnet motors of IE4 class in comparison with induction motors of IE4 or even IE3 class. Therefore, it is necessary to take into account efficiency of the motors at underload and it is needed to calculate the energy consumption during the actual load cycle. It should be noted, that the existing approach based on the Energy Efficiency Index (EEI) calculation does not provide information about absolute values of energy savings and cost savings, in contrast to the described approach. While choosing motors to run in the considered application it is also important to take into account that the motors with permanent magnets on the rotor have significantly higher price and very restricted starting capabilities comparing with induction motors. In addition, the production of rare earth magnets causes a significant environmental damage.

Energy efficiency, solar energy and cost saving opportunities in public tertiary institutions in developing countries: The case of KNUST, Ghana

High electricity consumption and corresponding cost in public tertiary institutions in many developing countries is a major challenge to their respective governments. Low efficiency electrical appliances are common in these institutions. In this work, studies have been conducted on sustainable energy efficiency measures and solar energy to reduce electricity cost in public tertiary institutions with a case study at the Kwame Nkrumah University of Science and Technology, Kumasi-Ghana. Energy audits as well as actual measurements of power consumptions of some selected electrical appliances using power quality analyzers were conducted. The result showed that there is total electricity savings opportunity of 163,400 kWh ± 5% per month by implementing energy efficiency retrofitting for the air-conditioners, lighting systems and ventilation fans for the faculty areas and the main administration buildings. This electricity savings translates to electricity cost reduction of about US$ 37,880 per month. Analysis also showed that the cost of conserved energy for the three electrical appliances are all lower than the utility tariff for KNUST. Considering large-scale solar PV integration in KNUST’s energy mix, the financial analysis has revealed that there is opportunity for electricity cost savings of US$ 69.1 million over a 20-year period with payback of 6 years.