Cost-effective Energy Consumption Research Articles

A Stackelberg-Cournot model to harmonize participants' interests in a smart grid

A “smart grid” refers to an advanced and modernized electrical grid system that incorporates various digital technologies, communication networks, and automation. In a smart grid, each subscriber’s energy consumption is monitored and updated by their smart meter, and cost-effective energy consumption scheduling is determined by two-way communication with other participants. In general, the interests among participants are naturally conflicted, and therefore the game-theoretic approach is frequently used to solve the problem. However, most previous studies focused on the game between a service provider and subscribers or game among service providers or subscribers. In this study, we propose an extended model consisting of two sub-games: 1) the Stackelberg game between a service provider (a leader) and subscribers (followers), and 2) the Cournot game among subscribers. Our model conducts an innovative energy consumption scheduling (ECS), especially harmonizing all participants’ aspects. It draws a unique Nash Equilibrium and achieves a more balanced and cost-effective demand management in the smart grid. Compared to the non-scheduling, our model reduces the subscriber’s cost and its standard deviation, on average, 20.5 % and 12.7 %, respectively. Also, the peak-to-average ratio (PAR) of energy consumption, the ratio between the highest energy consumption and the average energy consumption within a certain time frame, is decreased, on average, 24.3 %.

An Evaluation of ARIMA and Persistence Models in IoT-Driven Smart Homes

Commencing with the transformative fusion of Smart Home and Internet of Things (IoT) technologies, this study scrutinizes the efficacy of predictive modeling approaches, specifically the autoregressive integrated moving average (ARIMA) and persistence algorithms. The primary focus lies in their potential for forecasting and optimizing energy consumption dynamics within the intricate framework of smart homes. The investigation reveals a nuanced comparison between the proposed ARIMA and conventional Persistence models. Smart Home, emblematic of innovative living, integrates seamlessly with IoT, promising an intelligent and interconnected domestic ecosystem. To enhance energy efficiency, this study explores the ARIMA model's capabilities alongside the persistence algorithm. Notably, the proposed ARIMA model showcases exceptional prowess in forecasting, substantiated by a significantly lower compared to the Persistence model. The ARIMA model, with an Root Mean Square Error value of 0.03378, outshines the Persistence model with a higher Root Mean Square Error value of 0.158 when evaluated on the test dataset. This substantial reduction in emphasizes the superior performance of the ARIMA model, making it a compelling choice for time series forecasting tasks. Beyond quantitative metrics, the precision of the ARIMA model holds transformative potential, promising cost-effective energy consumption, proactive maintenance, and an elevated quality of life within smart homes. This research establishes a robust foundation for integrating advanced predictive modeling, particularly the ARIMA model, to enhance the efficiency, sustainability, and inhabitant satisfaction of smart homes in the era of IoT.

Cost effective energy consumption in a residential building by implementing demand side management in the presence of different classes of power loads

ABSTRACT In the present scenario, in order to meet the growing electricity demand, demand side management (DSM) is one among the various approaches used in smart grid. By applying DSM at the customer side, various benefits such as reduction in electricity cost, reduction in peak demand, and improvement in load factor can be achieved. This paper proposes a new architecture, namely, Intelligent Universal Load Management System (IULMS) to implement DSM based on load shifting approach for a residential building in the presence of different classes of loads. In this work, the objective is to minimize the cost of electricity consumption and there are several constraints imposed by the device types operating in the system and it is solved by Binary Grey Wolf Optimization algorithm. The objective function is non-linear in nature because the price of electricity varies each hour. A real-time system involving residential building users of Agrini Apartment in Madurai, Tamilnadu, India is discussed. Further, the potential of DSM is analyzed. The minimization of electricity consumption cost, peak demand, and Peak to Average Ratio (PAR) are validated for each test case.

Joint Energy Management and Energy Trading in Residential Microgrid System

The sustainability of the power systems assures consumers to have efficient and cost-effective energy consumption. Consumers' energy management is one of the solutions that in fact boosts the power system stability via efficiently scheduling the appliances. In addition to energy management, consumers fulfill their low-cost energy consumption using decentralized energy generation (such as solar, wind, plug-in hybrid electric vehicles, and small diesel generator). This decentralized energy generation and its trading among the prosumers and consumers help in the distribution grid stability and continuous supply. In this paper, the joint energy management and energy trading model is presented, which provides low-cost electricity consumption to the distribution system. The proposed framework is a twofold system. In the first fold, the distribution system is divided into a number of microgrids, where each microgrid electricity demand is managed using a unified energy management approach. While the local energy produced is traded among the microgrids in the second fold, through energy trading concepts that fulfill the consumers' demand without stressing the utility company. The results indicate that the proposed model reduced the electricity cost of the microgrids with maximum share of self-generation. Moreover, the results also indicate that each microgrid either fulfills its electricity demand from self-generation or purchases it from the nearby microgrid.

Sensor Communication Model Using Cyber-Physical System Approach for Green Data Center

Energy consumption in distributed computing system gains a lot of attention recently after its processing capacity becomes significant for better business and economic operations. Comprehensive analysis of energy efficiency in high-performance data center for distributed processing requires ability to monitor a proportion of resource utilization versus energy consumption. In order to gain green data center while sustaining computational performance, a model of energy efficient cyber-physical communication is proposed. A real-time sensor communication is used to monitor heat emitted by processors and room temperature. Specifically, our cyber-physical communication model dynamically identifies processing states in data center while implying a suitable air-conditioning temperature level. The information is then used by administration to fine-tune the room temperature according to the current processing activities. Our automated triggering approach aims to improve edge computing performance with cost-effective energy consumption. Simulation experiments show that our cyber-physical communication achieves better energy consumption and resource utilization compared with other cooling model.

A global analysis of residential heating and cooling service demand and cost-effective energy consumption under different climate change scenarios up to 2050

Climate change and energy service demand exert influence on each other through temperature change and greenhouse gas emissions. We have consistently evaluated global residential thermal demand and energy consumption up to the year 2050 under different climate change scenarios. We first constructed energy service demand intensity (energy service demand per household) functions for each of three services (space heating, space cooling, and water heating). The space heating and cooling demand in 2050 in the world as a whole become 2.1–2.3 and 3.8–4.5 times higher than the figures for 2010, whose ranges are originated from different global warming scenarios. Cost-effective residential energy consumption to satisfy service demand until 2050 was analyzed keeping consistency among different socio-economic conditions, ambient temperature, and carbon dioxide (CO2) emission pathways using a global energy assessment model. Building shell improvement and fuel fuel-type transition reduce global final energy consumption for residential thermal heating by 30% in 2050 for a 2 °C target scenario. This study demonstrates that climate change affects residential space heating and cooling demand by regions, and their desirable strategies for cost-effective energy consumption depend on the global perspectives on CO2 emission reduction. Building shell improvement and energy efficiency improvement and fuel fuel-type transition of end-use technologies are considered to be robust measures for residential thermal demand under uncertain future CO2 emission pathways.

EFFECTIVE USE OF INDUSTRIAL HEAT PUMP INSTALLATIONS MOLDING WORKSHOP OF THE ENTERPRISE «SEA SPECIALIZED PORT " NIKA- TERA " NIKOLAEV

To date, the development of equipment of air conditioning, ventilation and heating systems, and their efficiency and economy become more urgent. As this issue of energy resources on the world market is very serious, almost all manufacturers of equipment heating, ventilation and air conditioning systems strive to implement the most cost-effective energy consumption equipment on the market. Just after the hardware manufacturers, designers design and installation of institutions adopting a more rational and efficient energy schemes and principles of heating and air conditioning systems.Heat recovery and energy efficiency of administrative , industrial and residential buildings are provided with the use of recuperative , air handling and heat pump systems.The most rational solution for the production, the scheme has been proposed which is due to concept of the unit , allows the so-called regime of free-cooling , allowing the chiller to operate year-round , in all ambient temperatures Nikolaev city , where the production , as well as to exclude from the scheme and drycooller flow heater , which will lead to a reduction in energy costs and simplify installation commissioning.Lithuanian production company "Refra" made the selection and calculation of the thermal unit from free-cooling model "CW-HS11XXXI0D".Finalization of the chiller CW-HS11XXXI0D, were two three-way valve , propylene - glycol circuit , built- in flow and return pipe drycooller . These two valve switches the circulation of coolant from its own drycooller heat exchangers air heating system management and / or heatchanger storage tank hot water system of an office building of the production plant .The study claims that proper utilization of process heat in the workplace, can significantly reduce production energy costs and the cost of production equipment and equipment of the heating system as a whole , due to which , and reduce production costs , and the burden of all enterprises of the state as a whole.

D2S: Dynamic Demand Scheduling in Smart Grid Using Optimal Portfolio Selection Strategy

In this paper, we propose a dynamic demand scheduling (D2S) scheme-an effort toward cost-effective energy consumption at customers' end. The theory of optimal portfolio selection is adopted to generate customers' expected day-ahead energy demand graph called the weight graph, based on past days' history-energy demand, profit return, and corresponding risk. In such a scenario, the weight graph of energy ensures that the expected profit return and the corresponding risk to the customers are optimized. Consequently, we evaluate the dynamic scheduling scheme for optimizing the energy cost to the customers using the weight graph. Furthermore, the proposed scheme also assists in relieving the peak-demand on the grid, which, in turn, implies that the grid is capable of providing service to the scheduled appliances. The performance of the proposed scheme is evaluated with different performance metrics-peak-demand, demand variation, energy-cost, and utility of the customers. Simulation results show that the proposed dynamic scheduling scheme, D2S, yields improved performance than that with the existing ones-no scheduling and static scheduling. It also shows that the utility of the customers increases approximately 28.2% over the existing ones.

Efficient Scheduling of Scientific Workflows with Energy Reduction Using Novel Discrete Particle Swarm Optimization and Dynamic Voltage Scaling for Computational Grids.

One of the most significant and the topmost parameters in the real world computing environment is energy. Minimizing energy imposes benefits like reduction in power consumption, decrease in cooling rates of the computing processors, provision of a green environment, and so forth. In fact, computation time and energy are directly proportional to each other and the minimization of computation time may yield a cost effective energy consumption. Proficient scheduling of Bag-of-Tasks in the grid environment ravages in minimum computation time. In this paper, a novel discrete particle swarm optimization (DPSO) algorithm based on the particle's best position (pbDPSO) and global best position (gbDPSO) is adopted to find the global optimal solution for higher dimensions. This novel DPSO yields better schedule with minimum computation time compared to Earliest Deadline First (EDF) and First Come First Serve (FCFS) algorithms which comparably reduces energy. Other scheduling parameters, such as job completion ratio and lateness, are also calculated and compared with EDF and FCFS. An energy improvement of up to 28% was obtained when Makespan Conservative Energy Reduction (MCER) and Dynamic Voltage Scaling (DVS) were used in the proposed DPSO algorithm.

Adaptive Control and Reconfiguration of Mobile Wireless Sensor Networks for Dynamic Multi-Target Tracking

We propose the adaptive control and reconfiguration schemes for mobile wireless sensor networks (MWSN) to achieve timely and accurate mobile multi-target tracking (MMTT) with cost-effective energy consumption. In particular, our proposed schemes can detect the mobile multi-targets' random appearance and disappearance in the clutter environments with high accuracy and low energy cost. We develop the optimal mutual-information based techniques to adaptively control the reconfiguration of the proposed MWSN by designing the Distributed/Decentralized Probability Hypothesis Density (DPHD) filtering algorithms. By dynamically adjusting the sensors' states, including their positions and activations, our schemes can efficiently improve the observabilities of the tracked multi-targets. We further analyze the asymptotic performance of our proposed schemes by deriving the upper-bounds of the detection-error probabilities. Also presented are the performance analyses which validate and evaluate our proposed adaptive control and reconfiguration schemes for MWSN in terms of the multi-target states estimation accuracy, the energy-consumption efficiency, and the robustness to the interference/noise.

Distributed Lifetime Optimized Routing Algorithm for Wireless Sensor Networks

The network hub nodes consumed excessive energy and failed prematurely, thus it reduced the network lifetime. In order to solve the problem, distributed lifetime optimized routing algorithm (DLOR) for wireless sensor networks was proposed. Energy for transmitting data and neighbor node residual energy were considered comprehensively. Then new weight function was introduced and distributed asynchronous Bellman-Ford algorithm was also used to construct the shortest routing tree. Finally, data were gathered along the shortest routing tree to sink node. Simulation results show that DLOR algorithm can extend network lifetime and enable cost-effective energy consumption. Under certain conditions, DLOR algorithm outperforms PEDAP, GreedyDijkstra, LET, Ratio-w and Sum-w algorithms.