Optimize Electricity Consumption Research Articles

Business model for the optimization of energy consumption by VAC equipment in service sector buildings

Electricity consumption has been growing in recent decades, and it is estimated that air conditioning accounts for 30% of a building’s total electricity consumption. However, in hot climates this percentage may be even higher, which has led to the development of energy management techniques aimed at reducing the high electric bills paid by end users. This article proposes a business model for the commercialization of intelligent solutions for the optimization of electricity consumption by air conditioning equipment (VAC) in service sector buildings. The study used the Canvas methodology to validate the proposed model, in combination with other techniques such as SCAMPER, interviews, and discussion groups, among others, to validate the proposed business model aimed at the introduction of new techniques for the commercialization of air conditioning. Service-sector buildings exposed to the climate conditions of the Colombian Caribbean region were used as case study. The results summarize the characteristics of the business model, including the respective validations of the nine “Canvas” modules, which enabled reviewing two possible scenarios offering different alternatives for implementation of this initiative.

Optimization of Electricity Consumption Using Evolutionary Algorithms

This paper offers optimized values of electricity consumption for a smart home which has different appliances working round the year. Two evolutionary algorithms, Particle Swarm Optimization (PSO) and Cuckoo algorithm have been used to predict the optimal value of the energy consumption in a given weather. The results of both algorithms have been studied in different temperature slabs and further compared to formulate a hypothesis to explain the reason of a certain optimal value for a given weather. The dataset of smart homes was taken from Kaggle and it had the information of appliances as well as the weather. The redundant and unimportant factors have been eliminated using statistical techniques and the consumption data was applied on both the algorithms and the trends were studied. It was concluded that the optimal consumption values vary according to external factors, temperature playing a major role.

Optimization of Own Electricity Usage with Electricity Supply from Solar Cells at Darajat Geothermal Power Plant

At Darajat Geothermal Power Plant there is the use of the generator’s own electricity for equipment such as cooling water pumps, lubricating oil pumps, oil transfer pumps, lighting lamps, heaters and others. The cost of own-use electricity used by the equipment based on the selling price of electricity from geothermal power plants to PLN is 1,119.74 Rp / kWh. With the potential of solar radiation of 4.90 kWh/m2 per day, optimization of electricity consumption using solar cells and batteries will be carried out. The price of the solar cell is assumed to be 12,000,000 Rp/kW, the battery inverter is 2,000,000 Rp/kW and the battery is 2,350,590 Rp/kW. The lifespan of solar cell equipment and batteries is 20 years. This paper will be simulated in 4 cases and the result will be the price of electricity based on the value of capital divided by the total electricity produced by solar cells and batteries for 20 years. Case 1 when using solar cell day and night using batteries and the result of electricity price for case 1 is 718.648 Rp kWh. Case 2 when using solar cell day and night using its own electricity from the generator and the result of electricity for case 2 is 990.952 Rp/kWh. Case 3 when using solar cell and battery, at night using battery and the result of electricity for case 3 is 711.77 Rp/kWh. Case 4 when using solar cells and batteries, at night using electricity itself from the generator and the results of the case 4 electricity price is 978.005 Rp/kWh. Based on the 4 case simulations, the price of electricity will be cheaper if during the day using a solar cell plus battery and night using a battery, as in case 3, the price of own electricity power plant is 711.77 Rp/kWh.

Research on real-time pricing strategy for load serving entity based on simultaneous perturbation stochastic approximation

As one of the main participant in the electricity market, load serving entities (LSE) purchase electricity in the wholesale market, and provide different types of electricity package to users in the retail market. LSE’s profit mainly comes from the difference between the cost of purchasing electricity and the benefit of selling electricity. To reduce the cost, LSE can optimize electricity consumption characteristics of various types of users through real-time price adjustment, which can help to improve the shape of electricity purchase curve and implement demand side response. In this paper, a real-time pricing strategy based on synchronous perturbation stochastic approximation algorithm (SPSA) is proposed. This method considers the difference between diverse users synthetically. Depending on the historical electricity consumption and historical price data, the user side response function is constructed, and the real-time electricity price is formulated with the objective of minimizing load curve’s peak-valley difference. Finally, the effectiveness and advantages of the proposed method are verified by comparing the load curve and the power purchase cost before and after the price adjustment.

Life Cycle Assessment of Material Recovery from Pyrolysis Process of End-of-Life Tires in Thailand

It is estimated that around 600,000 tons of end-of-life tires are generated annually in Thailand. These waste tires will cause danger to the environment and human health if handled improperly. On the other hand, if managed with the proper technology, it will be transformed into valuable products. This research aims to evaluate the potential environmental impacts of a waste tire pyrolysis plant in Thailand by using the Life Cycle Assessment (LCA) method. The functional unit is defined as 1 ton of products from the pyrolysis process of waste tires. The system boundary consists of a pre-treatment and pyrolysis process (gate-to-gate). The LCA calculations were carried out using licensed SimaPro 9.0 software. At the impact assessment step, the ReCiPe2016 method both Midpoint (problem-oriented) and Endpoint (damage-oriented) were applied, and 7 impact categories were selected (global warming, fine particulate matter formation, terrestrial acidification, freshwater eutrophication, terrestrial ecotoxicity, freshwater ecotoxicity, and fossil resource scarcity). If the avoided products from the pyrolysis process, including pyrolysis oil, steel wire, and carbon black were taken into account, the characterization results show that 3 impacts: global warming, terrestrial ecotoxicity, and fossil resource scarcity have a negative value. While the other impacts still have a positive value resulted mainly from electricity consumption. When considering weighting end-point results, it found that human health impact was a major contribution with a totally negative value of -0.947 Pt. As a summary, the outcomes confirm that the utilization of pyrolysis avoided products and the optimization of electricity consumption in the process has the potential to drives pyrolysis to become an environmentally effective technology for end-of-tires management.

Internet of Things and LoRaWAN-Enabled Future Smart Farming

It is estimated that to keep pace with the predicted population growth over the next decades, agricultural processes involving food production will have to increase their output up to 70 percent by 2050. Precision or agriculture is one way to make sure that these goals for future food supply, stability, and sustainability can be met. Applications such as smart irrigation systems can utilize water more efficiently, optimizing electricity consumption and costs of labor; sensors on plants and soil can optimize the delivery of nutrients and increase yields. To make all this smart farming technology viable, it is important for it to be low-cost and farmer-friendly. Fundamental to this IoT revolution is thus the adoption of low-cost, long-range communication technologies that can easily deal with a large number of connected sensing devices without consuming excessive power. In this article, a review and analysis of currently available long-range wide area network (LoRaWAN)-enabled IoT application for smart agriculture is presented. LoRaWAN limitations and bottlenecks are discussed with particular focus on their effects on agri-tech applications. A brief description of a testbed in development is also given, alongside a review of the future research challenges that this will help to tackle.

Load shifting of a supplier based demand response of multi-class subscribers in smart grid

This paper presents a load shifting mechanism to address the problem of the overloaded electricity consumption requirement of different classes of electricity users, namely, the residential, commercial and industrial users, provided by a supplier in a smart grid. A real-time pricing model based the utility of each user group and the supply cost is given and an alternating direction method of multipliers is used to solve the model for obtaining the optimal real-time electricity price and consumption of each user group. A demand response (DR) algorithm-based different user groups, gives a priority load shifting rule to allocate the surplus optimal or reserved electricity consumption requirements of some user groups to other suppliers to keep the requirements within the capacity of the supplier. Numerical experiment is conducted to analyse the performance of the DR algorithm, which shows that the consumption requirement load shifting mechanism is reasonable and feasible.

Trust-less electricity consumption optimization in local energy communities

Optimizing energy consumption in local energy communities is one of the key contributions to the so-called smart grid. Such communities are equipped with rooftop photovoltaic power plants or other forms of small power plants for local energy production. In addition, a number of appliances allow for shiftable energy consumption, e.g., heat pumps or electric vehicle charging stations. The ability to shift is, however, dependent on customer preferences. In this paper, we present a trust-less approach for optimizing the electricity consumption in a local energy community given forecasts of energy production and customer demands, along with constraints for shiftable loads. In larger communities, appointing a single party for managing load curtailment requires a high level of trust. In the proposed trust-less approach, all parties can independently propose optimal solutions for this optimization problem and then globally agree one one solution that meets the defined requirements to the greatest extent.

Day-ahead electricity consumption optimization algorithms for smart homes

The electricity consumption optimization and the advanced tariff schemes are some of the key factors in the demand response programs, which target several aspects such as the minimization of the consumption peak, a reduced electricity bill, the reduction of the consumption, etc. By various incentives, the electricity consumers become more and more active, thus being able to change their consumption behavior as a consequence of the tremendous progress of IT&C and sensor technologies. In this paper, we propose four novel algorithms that are able to minimize the consumption peak for eleven complex smart homes with over three hundred appliances and eight roof-PhotoVoltaic (PV) systems connected to the Internet and smart meters which allow different resolutions for the consumption records. The four algorithms are executed in parallel and the best option is chosen for the day-ahead electricity consumption optimization, while the electricity generated by the PV systems is shared at the community level, further improving the results of the optimization algorithms. In addition, several Time-of-Use (ToU) tariffs are implemented to assess the electricity payment and verify the efficiency of the proposed algorithms.

Distributed genetic real-time pricing for multiseller-multibuyer smart grid based on bilevel programming considering random fluctuation of electricity consumption

As reform and deregulation in the power industry proceed, more than one utility company has emerged in the power grid. In addition, due to the random fluctuation of electricity consumption, the power grid becomes more complex. New appropriate management strategies are required to handle the future smart grid. The real-time pricing (RTP) mechanism is an incentive way to increase energy usage efficiency. In this paper, considering the effect of the random fluctuation of electricity consumption, we propose a distributed genetic RTP scheme for smart grid with multiple utility companies and users based on expectation bilevel programming. We discuss the properties of the model and transform it into a deterministic optimization problem. Using a distributed genetic algorithm (GA), we find the optimal strategy for electricity supply and consumption and obtain the RTP. With this approach, subproblems can be solved individually. In addition, at each iteration, the information exchanged in the integrated system only consists of prices, the optimal electricity consumption, etc., which can be provided by each utility company and user respectively. Therefore, the distributed GA not only helps to protect the privacy of utility companies and users but also lowers the complexity of the computation. Simulation results validate the proposed distributed genetic RTP can significantly reduce peak time loading and efficiently balance system energy distribution while maximizing benefits for both utility companies and users.

Real-time pricing method for smart grids based on complementarity problem

Considering a demand response (DR) based social welfare maximization model, a complementarity problem based on the Karush-Kuhn-Tuker condition is described, which is a non-dual method for determining real-time price for smart grids. The Lagrange multiplier in the dual method, which is used to determine the basic electricity price, is applied in the model. The proposed method computes the optimal electricity consumption, price and production. According to the electricity price, users can arrange their electricity equipment reasonably to reduce the consumption pressure at peak time. The model aims to encourage users to actively participate in the DR and realize peak cutting and valley filling. In addition, the model considers different utility functions representing three types of users. Finally, a Jacobian smoothing version of Newton method is used to solve the model. Statistical simulations of the model validate the rationality and feasibility of the proposed method.

Optimization of the operation of the anthropomorphic manipulator in a three-dimensional working space

ABSTRACTThis article presents the use of the particle swarm method to optimize the working cycle of an anthropomorphic manipulator. The working space contained stationary obstacles which made the working cycle difficult. Three objective functions were adopted in the study, concerning trajectory minimization, reduction of electricity consumption and multi-criteria optimization (minimization of both trajectory length and electricity consumption). The effects of selected parameters on the particle swarm optimization (PSO) algorithm and the results of the examinations were analysed.

Sliding Time Window Electricity Consumption Optimization Algorithm for Communities in the Context of Big Data Processing

Big data frameworks enable companies from various fields to build models that allow them to increase profit margins by improving decision making at different levels (middle management, senior management, and board) or by attempting to boost sales by customizing consumers’ experience based on their history and feedback. Institutions and other entities also use big data coming from all kinds of sensors, data that can be used to detect, in real time or in retrospect, possible problems (e.g., frauds, malfunctions, and supply shortages), or to identify patterns and trends. In this paper, we organize large volumes of community electricity consumption data coming from smart meters, smart plugs, and other sensors, but also data regarding consumers’ preferences in order to assist them to dynamically optimize their electricity consumption. In this regard, we develop a novel optimization approach that re-schedules every fifteen min the appliances for residential consumers to reduce both the consumption peaks and the payments at the community level. The consumers send their day-ahead schedule that is optimized and further implemented to some extent. Thus, we monitor the electricity consumption via sensors and smart meters and dynamically adjust the schedule in case the real consumption deviates from the optimized plan, considering appliances constraints and consumers’ preferences. Every fifteen min, the algorithm evaluates the differences between the optimized schedule and the actual consumption and controls the operation of the interruptible appliances to stick with the day-ahead schedule as much as possible.

Robust optimization based optimal chiller loading under cooling demand uncertainty

Optimization of electrical power consumption in multi-chiller system leads to save more energy in building or industrial locations. Also, this optimization problem is one of the most important issues in multi-chiller system. Furthermore, uncertainty modeling of cooling demand is necessary because of variation in cooling demand should be considered. Therefore, this work proposes a robust optimization approach for uncertainty modeling of cooling demand in order to obtain robust chiller loading in the uncertain environment which cooling demand is supplied by multi-chiller system. Minimizing of electrical power consumption in multi-chiller system is considered as objective function. The proposed robust scheduling of multi-chiller system is modeled as non-linear programming which is solved via CONOPT solver under General Algebraic Modeling System (GAMS) optimization software. The proposed optimization model is studied in the deterministic and robust optimization strategies and obtained results are compared with each other. Also, the effects of changes in the robust control parameter are analyzed on optimal chiller loading which decision maker can select the decision without risk as risk-neutral strategy via deterministic method or the most robust decision as risk-averse strategy via robust optimization approach. Comparison results show that capability of proposed approach in the uncertain environment.

Erratum to: On the Method of Determining Optimal Electricity Consumption from an Electric Traction Network

Erratum to: On the Method of Determining Optimal Electricity Consumption from an Electric Traction Network

Analyses of Distributed Generation and Storage Effect on the Electricity Consumption Curve in the Smart Grid Context

The householders’ electricity consumption is about 20–30% of the total consumption that is a significant space for demand response. Mainly, the householders are becoming more and more active and interested in diminishing their expenses related to the electricity consumption, considering different rates of the advanced tariffs. Therefore, in the smart grid context, especially for prosumers with energy sources and storage devices (SD), the electricity consumption optimization becomes attractive since they obtain significant benefits. On the other hand, the electricity suppliers design appropriate tariffs in order to reduce the consumption peaks and avoid the occurrence of new peaks. Based on the effect of these tariffs on consumers’ behavior, the stress on generators decreases and the electricity suppliers improve the demand forecast and adjust their strategies on the market. In addition, the grid operators are interested in the minimization of the consumption peak that leads to loss reduction and avoidance of congestions that would ensure at least the delay of the onerous investment in grid capacities. In this paper, we will run several scenarios for electricity consumption optimization in the context of smart grid that includes: sensors, actuators, smart meters, advanced tariff schemes, smart appliances and electricity home control applications. Our goal is to analyze the effect of the Renewable Energy Systems (RES) distributed generation (such as photovoltaic panels—PV) and storage on the consumption curve. The results show that consumption optimization with RES distributed generation and SD brings sustainable development of the power systems and significant benefits from the consumption peak and savings point of view.

Intelligent video surveillance for real time energy savings in smart buildings using HEVC compressed domain features

This work presents a unique approach to utilize the existing video surveillance infrastructure to optimize electricity consumption in large indoor spaces such as library reading halls, waiting rooms, indoor sports complex, large dormitories etc. The proposed method extracts features from the encoder loop of the digital video recorder (DVR) to analyze the foreground activity. High Efficiency Video Coding (HEVC) is chosen as the target video codec, as it is going to be the most widely deployed video codec in the future DVRs. The bit rate efficiency of HEVC is almost double in comparison to H.264/AVC for same video quality [17]. The proposed method utilizes only compressed domain parameters from video stream such as motion vectors clustering information, block partitioning modes etc., which allows to develop algorithms with relatively lower computational complexity and memory requirement than pixel based methods thus achieving real time performance.

Informatics Solution for Energy Efficiency Improvement and Consumption Management of Householders

Although in 2012 the European Union (EU) has promoted energy efficiency in order to ensure a gradual 20% reduction of energy consumption by 2020, its targets related to energy efficiency have increased and extended to new time horizons. Therefore, in 2016, a new proposal for 2030 of energy efficiency target of 30% has been agreed. However, during the last years, even if the electricity consumption by households decreased in the EU-28, the largest expansion was recorded in Romania. Taking into account that the projected consumption peak is increasing and energy consumption management for residential activities is an important measure for energy efficiency improvement since its ratio from total consumption can be around 25–30%, in this paper, we propose an informatics solution that assists both electricity suppliers/grid operators and consumers. It includes three models for electricity consumption optimization, profiles, clustering and forecast. By this solution, the daily operation of appliances can be optimized and scheduled to minimize the consumption peak and reduce the stress on the grid. For optimization purpose, we propose three algorithms for shifting the operation of the programmable appliances from peak to off-peak hours. This approach enables the supplier to apply attractive time-of-use tariffs due to the fact that by flattening the consumption peak, it becomes more predictable, and thus improves the strategies on the electricity markets. According to the results of the optimization process, we compare the proposed algorithms emphasizing the benefits. For building consumption profiles, we develop a clustering algorithm based on self-organizing maps. By running the algorithm for three scenarios, well-delimited profiles are obtained. As for the consumption forecast, highly accurate feedforward artificial neural networks algorithm with backpropagation is implemented. Finally, we test these algorithms using several datasets showing their performance and integrate them into a web-service informatics solution as a prototype.

Optimización del Consumo Eléctrico de los Sistemas de Iluminación en Espacios Interiores de la Universidad Técnica de Ambato

En este artículo se presenta un estudio de optimización del consumo eléctrico del sistema de iluminación en espacios interiores de la sala de investigación del edificio principal de la Facultad de Ingeniería en Sistemas, Electrónica e Industrial de la Universidad Técnica de Ambato. El estudio de optimización del consumo eléctrico se basa en el análisis comparativo del diseño y simulación de dos diferentes sistemas de iluminación: el actualmente existente, conformado por luminarias fluorescentes, y el propuesto a instalar, conformado por luminarias LED. El diseño de los sistemas de iluminación evaluados debe cumplir la normativa de iluminación en espacios interiores UNE-EN 12464-1, garantizando los niveles mínimos de iluminancia. Para el diseño y simulación de los sistemas de iluminación, anteriormente mencionados, se empleó el software DIALux, que permite simular los sistemas de iluminación y verificar que se cumplan las especificaciones y recomendaciones, establecidos en la normativa vigente. Finalmente, se presenta un análisis comparativo del consumo eléctrico y del coste económico de los sistemas de iluminación evaluados, escalados al sistema de iluminación de la Facultad de Ingeniería en Sistemas, Electrónica e Industrial, FISEI, de la Universidad Técnica de Ambato, UTA.

The Fuzzy Logic Method to Efficiently Optimize Electricity Consumption in Individual Housing

Electricity demand shifting and reduction still raise a huge interest for end-users at the household level, especially because of the ongoing design of a dynamic pricing approach. In particular, end-users must act as the starting point for decreasing their consumption during peak hours to prevent the need to extend the grid and thus save considerable costs. This article points out the relevance of a fuzzy logic algorithm to efficiently predict short term load consumption (STLC). This approach is the cornerstone of a new home energy management (HEM) algorithm which is able to optimize the cost of electricity consumption, while smoothing the peak demand. The fuzzy logic modeling involves a strong reliance on a complete database of real consumption data from many instrumented show houses. The proposed HEM algorithm enables any end-user to manage his electricity consumption with a high degree of flexibility and transparency, and “reshape” the load profile. For example, this can be mainly achieved using smart control of a storage system coupled with remote management of the electric appliances. The simulation results demonstrate that an accurate prediction of STLC gives the possibility of achieving optimal planning and operation of the HEM system.