Reduce Electricity Costs Research Articles

Possibilities for reduction of energy consumption by replacing public lighting with LED lighting: Case study of Priboj municipality

Electric energy consumed by the public lighting represents a significant item in the city public sector energy consumption structure, which includes public buildings, public transportation and public lighting. Public lighting system is one of the most important public utility activities which is under control of local governments, which pay the costs of electricity, as well as maintenance costs. Public lighting affects the quality of life of citizens in cities through, among other things, reduction of traffic accidents by up to 35%, personal safety and reduction of social approaches. Cities and municipalities in Serbia are predominately using sodium and mercury bulbs for public lighting, but have been gradually replacing them with LED bulbs in the recent years. LED technology is a satisfactory, cost-effective solution due to low energy consumption, long service life, reduced investment and maintenance costs. Thus, there is not only a reduction in energy consumption of up to 70%, but also a large reduction in CO2 emissions. The modernization of the public lighting system is, in addition to being a useful means of achieving significant energy savings, also a means of improving the quality of lighting itself. The paper shows how much it is possible to reduce electricity consumption, and thus CO2 emissions, by replacing existing lighting with LED lighting. There are 2530 light bulbs in the public lighting system of the municipality of Priboj. Until recently, mercury high pressure bulbs dominated - there were 1241 of them, compared to 743 sodium ones and 546 LED lamps. After the reconstruction of public lighting, all light bulbs are LED. The paper compares the consumption of electricity for the four months of 2019 and the same four months of 2020. It is shown that electricity consumption for these four months was reduced by an average of 55%, the average reduction in CO2 emissions was 55%, while the reduction in electricity costs was an average of 58%.

An Advanced Satisfaction-Based Home Energy Management System Using Deep Reinforcement Learning

Home energy management (HEM) systems optimize electricity demand of appliances according to the price-based demand response (DR) programs. Undoubtedly, customer satisfaction is of such importance that if not taken into consideration, it prevents customers from participating in the DR. HEM systems suffer from high nonlinearity due to the variety of smart appliances and different criteria for customer satisfaction. In this paper, an advanced satisfaction-based HEM system using deep reinforcement learning is proposed to hourly schedule the controllable and time-shiftable appliances, including electric vehicle, air conditioner, and lighting system as controllable loads and washing machine, and dishwasher as time-shiftable loads. The proposed framework deploys a Deep Q-Network (DQN) method. Regarding customer dissatisfaction, this paper takes into consideration nonlinear precise functions. The Kano model for EV departure SoC, charging duration and lighting system satisfaction, desired temperature span for air conditioner, and the desirable operation period, waiting time, and consecutive mode of dishwasher and washing machine are taken into account. The proposed HEM system is applied to a smart home, and the results are compared with those of the Q-Learning algorithm. Numerical results prove the effectiveness of the proposed HEM system in reducing electricity cost and customer dissatisfaction, as well as the superiority of DQN over Q-Learning as well.

Modeli korišćenja električne energije kupaca-proizvođača

In light of the sharp and significant increase of the electricity price on the global market in 2021, the concept of electricity production for the own needs gained importance. This work will analyze the possibilities of reducing electricity costs for end customers who become prosumers. It will also present ways of managing prosumers consumption in order to more rationally use produced electricity and improve energy efficiency. In addition, the impact of prosumers on total electricity generation from renewable sources in the Republic of Serbia will be discussed. To that end, special attention shall be on different models of assessment of total electricity produced in prosumers facilities, in accordance with the Law on the Use of Renewable Energy Sources. At the end, the results of the analyses will be presented, as well as possibilities for future development of the prosumers concept, in order to achieve the goals in faster and more successful manner, in accordance with the commitments made in international agreements, which will be implemented in the Integrated National Energy and Climate Plan. In addition, when conducting research, special focus shall be on international practice in this area.

Blockchain-Based Privacy Preserving and Energy Saving Mechanism for Electricity Prosumers

With the development of distributed and renewable energy resources and smart grids, energy management systems that allow electricity prosumers to schedule their power usage, are seen as a prominent solution for reducing electricity costs. This paper presents a novel blockchain-based mechanism to incentivize prosumers to save energy, while preserving their privacy. In the proposed mechanism, each prosumer utilizes an energy management system that is based on the percentage power change (PPC) at each hour of the day. The use of PPC values allows the proposed blockchain to preserve the privacy of the prosumers as no sensitive information is shared. The calculated PPC values are shared among the prosumers. The prosumer with the minimum PPC value is selected as the validator of the blockchain, which is responsible for creating the next block of the blockchain. The problem of approving the validator, by other prosumers, is formulated using a novel zero-metric weighted average consensus. The communication model required to reach this consensus is investigated and the consensus value is analytically derived. Multiple test systems with varying number of prosumers are simulated and analyzed. The results demonstrate the capability of the proposed mechanism to sustain energy while preserving privacy in a scalable manner.

HPC Data Center Participation in Demand Response: an Adaptive Policy with QoS Assurance.

Demand response programs help stabilize the electricity grid by providing monetary stimulus to consumers if they regulate their power consumption following market requirements. Regulation service, a market that requires participants to regulate power by following a signal updated every few seconds, is particularly beneficial to HPC data centers since data centers are capable of increasing/decreasing power consumption owing to the flexibility in running workloads and the availability of power control mechanisms. While prior works have explored how data centers can provide regulation service reserves, Quality-of-Service (QoS) provisioning for the jobs running at the data centers has not been considered. In this work, we propose an Adaptive policy with QoS Assurance that enables data centers to participate in regulation service programs with assurance on job QoS. Our policy regulates data center power through job scheduling and server power capping. QoS assurance is achieved by applying a queueing-theoretic result to our job scheduling strategy. We evaluate our policy by experiments on a real cluster. Our results demonstrate that the proposed policy reduces electricity costs by 25-56% while providing QoS assurance. On the other hand, the baseline policies cannot meet QoS constraints in 9 of the 14 workload traces tested.

A cross-regional joint operation control method of small power photovoltaic power grid and municipal power grid

Aiming at the problems of high-failure rate of transmission lines and large fluctuation of output waveform in traditional methods, a control method for cross-regional joint operation of low-power photovoltaic grid and municipal grid based on fuzzy logic control is designed. This method combines the overall topological structure of the power supply system and installs a protection circuit to reduce electricity costs. In the entire combined power supply system, the fuzzy logic control method is used to obtain the distance distribution function of the fault nodes in different areas of the power grid, and the effective control of the power grid is realised by adjusting the grid deviation coefficient. The results show that the failure rate of the transmission line is reduced from 6 to 3%, the output waveform fluctuates steadily and the grid operation status is relatively stable, which guarantees the safe operation of the grid.

Energy management system of microgrids in grid-tied mode: A hybrid approach

ABSTRACT This paper proposes a proficient hybrid approach based energy management system (EMS) of microgrid (MG) in grid-tied mode. The microgrid connected system like photovoltaic (PV), wind Turbine (WT), and microturbine (MT) battery. The major intention of the proposed approach is “to reduce the cost of electricity and enhancing the power flow (PF) among the source and load side.” The proposed method is the combination of Artificial Neural Network (ANN) and Hybrid Whale Optimization (HWO) approach, hence it is known as ANN-HWO. The necessary load requirement of grid connected microgrid scheme is constantly monitored by ANN. The HWOA develops the optimal combination of the microgrid based on the predicted load demand. Moreover, the two methods for microgrid energy management have been employed to minimize the impact of renewable energy predicting faults. The first method is focused on the various renewable energy sources (RESs) programming to reduction of electricity cost while the MG function. The second method is to balance the PF and minimize the forecasting errors impacts depending upon rule outlined from the scheduled power reference. The proposed technique is activated in MATLAB/Simulink site, and then the efficiency is classified with and without grid of MG structure. The effectiveness can be compared based on cost analysis and power generation of PV, MT, WT, and battery of existing methods. The power generation from the various sources based upon the ANN-HWO and existing methods is evaluated. Also, the total cost of the system also analyzed with different existing methods. The statistical evaluation, like mean, median, and standard deviation is also analyzed. The accuracy, specificity, recall and precision, RMSE, MAPE, MBE and consumption time of ANN-HWO, and existing techniques are also conducted. The mean, median, and standard deviation for 50 numbers of trails of the proposed technique is 1.3116, 1.2931, and 0.0354. The mean, median, and standard deviation for 100 numbers of trails of the proposed technique is 1.3232, 1.2786, and 0.0946. The RMSE, MAPE, MBE, and execution time for 50 numbers of trails of the proposed technique is 7.840, 0.748, 0.9971, and 1.011s. The RMSE, MAPE, MBE,and execution time for 100 numbers of trails of the proposed technique is 5.21, 0.93, 1.93, and 2.005 s. The proposed technique in 100, 200, 500, and 100 numbers of trails, the efficiency is 99.9673%, 99.7890%, 99.89402%, and 99.77879%.

Research on optimal scheduling decision of multi‐microgrids based on cloud energy storage

In microgrids, energy storage systems (ESS) can mitigate the intermittency and uncertainty associated with large amounts of renewable energy connected to the grid. Cloud energy storage (CES) is a solution proposed by scholars over the last two years. It provides energy storage and trading services that can alleviate high investment costs and small-scale wind and light abandonment problems. However, large-scale energy mutualisation and fine scheduling of energy become significant challenges for further development. This study proposes a CES scheduling architecture based on multi-microgrids. CES extends the scope of power trading from a single microgrid to multi-microgrids, with the microgrid capable of interacting with neighbouring microgrids for power and reducing the transmission pressure on the larger grid. In addition, this study defines the game strategy for power trading between cloud storage operators, considering the reduction in electricity costs due to dishonest behaviour by users. The simulation results indicate that the proposed dispatching architecture and strategy can effectively improve the consumption rate of new energy sources, mitigate grid fluctuations, and further reduce electricity costs for users while also being resilient to dishonest user behaviour.

Advanced feasibility cuts in decoupled cooperative optimization of power flow

Solving the optimal power flow problem in a cooperative way between power generators and industrial users can reduce electricity costs more effectively than price-based demand side management. In previous work, we recently developed a decomposition-based algorithm based on Benders-type cuts to solve cooperative optimal power flow problems, which allows to protect sensitive load information. However, the algorithm suffers from unfavorable scaling behavior when the number of cooperating load entities increases. Herein, we improve the quality of the cutting planes that cooperating electricity users generate for the grid in lieu of sharing their dynamic process models. After comparing different cutting strategies and combinations thereof, we develop a tailored cutting strategy. This strategy improves the scaling behavior of our decomposition-based algorithm with multiple cooperating electricity user locations drastically. We obtain speedups factors up to almost 60 compared to our algorithm with the initial cutting strategy.

Comparative Assessment of the Impact of Electricity Consumption in Different Economic Sectors on the Economic Development of the EU Member States

Recently, the Member States of the European Union (EU) have found themselves in a controversial situation. On the one hand, national economic development is barely possible without increasing electricity consumption, whereas on the other we are facing increased use of natural resources (coal, oil, gas, wood), thermal effects, pollution and risks to human health. The European Green Deal is a response to the currently observed negative trends. The strategy aims to accelerate the economic development of the EU Member States, thus reducing electricity consumption. Objectives may include both the national economy and the electricity generation sector by applying advanced technologies and introducing innovations that increase output efficiency while reducing electricity costs. Assessing the current situation is vital for the successful implementation of the European Green Deal, i.e., by comparing the impact of electricity consumption on the economic development of the Member States. Thus, combining indicators for national economic development and the extent of electricity consumption into a single aggregate is necessary because electricity greatly affects economic development. The proposed methodology allows dividing the analysed EU Member States into three groups, in line with the degree of national economic development and the scope of electricity consumption in their economy sectors.

A new energy storage sharing framework with regard to both storage capacity and power capacity

Energy storage systems have received widespread attention due to their advantages on rapid response, smooth fluctuations, and the reduction of temporal and spatial imbalance. At present, most researchers mainly consider the allocation of energy storage capacity while using an average allocation of the power capacity, which may lead to conflicts among users when executing the energy sharing strategies for the case with limited power capacity. In order to better improve energy efficiency and reduce electricity costs, this paper proposes an energy storage sharing framework considering both the storage capacity and the power capacity. Since each prosumer intends to minimize its costs which depend on the behavior of other prosumers, the relationship among prosumers forms a non-cooperative game. By using an algorithm that combines the alternating direction multiplier method and heavy ball method, a generalized Nash equilibrium is found to serve as an energy sharing strategy. Simulation studies and comparisons show that the proposed energy storage sharing framework driven by a dynamic electricity price mechanism can reduce prosumers’ net demands, electricity costs and improve the efficiency of energy storage utilization.

Development and Implementation of an IoT-Enabled Optimal and Predictive Lighting Control Strategy in Greenhouses.

Global population growth has increased food production challenges and pushed agricultural systems to deploy the Internet of Things (IoT) instead of using conventional approaches. Controlling the environmental parameters, including light, in greenhouses increases the crop yield; nonetheless, the electricity cost of supplemental lighting can be high, and hence, the importance of applying cost-effective lighting methods arises. In this research paper, a new optimal supplemental lighting approach was developed and implemented in a research greenhouse by adopting IoT technology. The proposed approach minimizes electricity cost by leveraging a Markov-based sunlight prediction, plant light needs, and a variable electricity price profile. Two experimental studies were conducted inside a greenhouse with “Green Towers” lettuce (Lactuca sativa) during winter and spring in Athens, GA, USA. The experimental results showed that compared to a heuristic method that provides light to reach a predetermined threshold at each time step, our strategy reduced the cost by 4.16% and 33.85% during the winter and spring study, respectively. A paired t-test was performed on the growth parameter measurements; it was determined that the two methods did not have different results in terms of growth. In conclusion, the proposed lighting approach reduced electricity cost while maintaining crop growth.

Analysis of Potential of More Efficient Electricity Cost Management Control by Applying Measures to Improve Energy Efficiency

Abstract The electricity supplier, having direct contact with end consumers, has significant potential for contributing to the achievement of country’s strategic goals related to increasing the share of electricity production from renewable energy sources and promoting efficient use of electricity. This paper presents the concept of conducted analyses of energy efficiency at the customers of JP Elektroprivreda BH d.d. Sarajevo for the purpose of identifying consumers where it would be useful to apply some of the energy efficiency measures and/or peak load management, all for reducing electricity costs. The results of the analysis are presented in the paper. Special attention is given to the techno-economic analysis of the application of measures used to improve the energy efficiency. The results of the analyses show that the shortest payback period is achievable by adequate waste heat recovery in compressors and refrigeration systems, reactive power compensation, control of motor drives of pumps and fans, and the replacement of motors with a higher efficiency class.

Oversized Electrical Appliance Impacts on Condominium Energy Efficiency and Cost-Effectiveness Management: Experts’ Perspectives

A direct use approach incorporating a cost approach assumed that replacing oversized electrical appliances with those better fit to actual energy consumption can reduce energy consumption, optimizing capacities of the new appliances to the maximum while reducing electricity costs. This study aimed to verify the assumption that the size of appliances has impacts on energy consumption and cost effectiveness. A mixed-method approach included these instruments for data elicitations (i.e., a questionnaire, data records of 485 transformers, two assessments of condominium technical caretakers, and two in-depth interviews of electrical engineering experts). The findings revealed that most condominiums installed electric appliances that are too large for their actual energy usage, which lies between 5.4% and 7.1% of the capacity. This study therefore proposed a total cost reduction of 54% by downsizing these appliances (i.e., MV Switchgear 2 sets, dry type transformer 2 sets 80,000, LV Cable 10 m. (XLPE), main distribution board, Busduct (MDB-DB), generator (20% of Tr.), and generator installation). Even though this analysis is limited to Bangkok, Thailand, this case may contribute decision-making on electrical appliance selection at early stage of investment or to downsize the currently installed appliances for the more energy efficient and cost-effective management of condominiums around the world.

Methodological development for the optimisation of electricity cost in cement factories: the use of artificial intelligence in process variables

Cement factories require large amounts of energy. 70% of the variable cost goes to energy—33% to kiln thermal energy and 37% to electrical energy. This paper represents the second stage of a broader research study which aims at optimising electricity cost in a cement factory by means of using artificial intelligence. After an analysis of the different tools that could be highly useful for the optimisation of electricity cost, for which a systematic review of the literature and surveys and an expert panel of 42 professionals in the cement sector were carried out, a methodology was developed in order to reduce electricity cost by optimising not only different variables of the production process, but also regulated electricity costs and electricity market costs. Artificial neural networks and genetic algorithms will be the tools to be used in this methodology, which can be applied to any cement plant in the world, and, by extension, to any electro-intensive consumer. The innovation of this research work is based on the use of a methodology that not only combines two different variables at the same time—process variables and regulated prices—but also makes use of artificial intelligence tools techniques.

Design and implementation of an AI-based & IoT-enabled Home Energy Management System: A case study in Benguerir — Morocco

Home Energy Management Systems (HEMS) are of great importance today and have attracted a great deal of interest from both, academic researchers, and industrial engineers. These systems are considered as the intersection point between the smart grid and the smart home. Indeed, they can communicate with the user, home appliances, on-site generation sources, storage devices, and the grid operator to make dynamic decisions enabling intelligent and efficient energy management.This work aims to design and implement a smart HEMS that ensures renewable energy integration and energy efficiency improvement in residential buildings. The system designed in this work has been implemented as part of a pilot project in a testbed house of the Smart Campus — Green & Smart Building Park, Benguerir, Morocco.The proposed HEMS framework is based on two control strategies that work jointly: the first concerns the scheduling and control of power dispatch among generation, consumption, and storage agents (Supply-Side Management), while the second concerns the scheduling and control of flexible appliances for optimal load profile modulation (Demand-Side Management). The management of energy flows is based on grid electricity price, forecasting data (PV generation and weather conditions) and user preferences. The two designed control strategies are combined into an AI-based multi-objective optimization algorithm that minimizes costs and maximizes comfort level simultaneously.The obtained results validate the effectiveness of the proposed algorithm and indicate that it would significantly increase the penetration of PV energy for self-consumption and reduce electricity costs, while ensuring a proper compromise between monetary spending and comfort level. The large-scale implementation of such systems would help decarbonize residential energy sector through higher renewable energy integration and energy efficiency improvement in buildings.

Mass-producible slit coating for large-area electrochromic devices

The recent trend of some countries opting to ban the sale of new gasoline and diesel vehicles has led to an increase in the use of electric vehicles and automated driving. In an automobile, windows are the largest source of heat energy between the interior and exterior of the vehicle. By modulating the window material, we can precisely control the amount of heat from sunlight that is allowed to enter the car, reduce the load on heating and cooling. Thus, suppress the energy loss associated with air conditioning by introducing technology to control heat energy in windows, which can also help reduce electricity costs. To this end, our research group has been developing complementary electrochromic devices (ECDs) using Prussian blue and tungsten oxide, which consist of nanoparticles dispersed in aqueous solvents to make inks. Spin coating is used for film deposition at the laboratory level; however, it has a major limitation in terms of uniformity. Therefore, in this study, we fabricated functional thin films by using these inks with slit coating, which is a commercial process. The results obtained confirm that slit coating can be used to deposit approximately 1-μm-thick films as fast as within 20 s and even on large G2 size substrates; further, the uniformity of the films measured based on chromaticity and haze is high. In addition, this effect improves the response time of the ECD. As a future direction, this can be applied to flexible ECD, etc.

Variable Voltage Control of Three-Phase Induction Motor for Energy Saving

Energy saving is one of the most important factor for industrial and commercial applications. In the industries, Induction motors are the mostly preferred due to their reliability, robustness, price and rugged construction. The largest percentage of consumed energy in industrial is consumed by induction motors. So, Energy conservation in induction motor drive leads to reduction in power consumption and electricity cost. This paper describes energy saving technique for squirrel cage induction motor. The energy saving is achieved by controlling the voltage applied to the stator winding using the three phase AC voltage controller. The simulation verifications are done in MATLAB/Simulink. The results show that the proposed scheme gives rise to the considerable energy-savings The rated efficiency of an induction motor is high when it operates under the full load. In general, the rated efficiency is larger than 75%. Although, the operating efficiency of the induction motor drive system will be low if an induction motor is operating at light loads or does not selected correctly. So, that the improvement in the energy efficiency of induction motor drives refers to huge energy saving

A cost-benefit analysis of implementing a 54 MW solar PV plant in a South African platinum mining company: A case study

A reliable and secure supply of energy is a prerequisite for adequate output and economic growth – especially in a platinum mining company. With exponential tariff increases, inadequate power supply leading to power cuts, and a carbon tax introduction, this study compared the costs with benefits by implementing a 54 MW solar photovoltaic (PV) plant. Two scenarios were compared over the same 20-year period in a case study of a South African platinum mining company operating in the platinum belt of Rustenburg. The first scenario was grounded upon the decision to proceed with the conventional manner of sourcing electricity from Eskom, South Africa’s power utility. The second scenario assessed the implementation of a 54 MW solar PV plant. The findings reveal that the company could generate 2 439 753 MWh of clean energy over 20 years with an investment of ZAR 910 857 920, giving a ZAR 563 205 994 (11%) carbon tax saving and a ZAR 5 614 426 335 (10%) reduction in electricity costs. Further, installing the solar PV plant could reinforce the company‘s dedication to protecting the environment and creating job opportunities through the employment of staff to install and maintain the plant.

Single contract power optimization: A novel business model for smart buildings using intelligent energy management

Typically, residential buildings neither allow flexibility in the individual contract power capacity nor considers buildings as unique electricity consumers. In this work, a smart building is designed that each electricity customer has flexible contract power and the whole collective residential building has a single contract power. A management entity is considered to manage all energy resources of the building such as the photovoltaic generation, electric vehicles, and battery energy storage system, taking into consideration the consumption from apartments and common services, to minimize the electricity bill. Hence, the best/optimal contract power capacity will contribute to minimizing electricity costs. Therefore, finding the optimal decision of the contract power value has received a significant role from the energy management in smart buildings. In this paper, a mixed binary optimization problem is formulated in which not only the optimal value of contract power is yield but the optimal schedule of the electric vehicle/battery storage charge and discharge are found, taking into consideration the photovoltaic generation and load consumption profiles. The proposed model is implemented for three scenarios, and the obtained results show that the model efficiency has a high performance with a significant electricity cost reduction, around 47%. The results pointed that using an optimal value of single contract power and intelligent management system, the building electricity costs decrease remarkably.