Energy Consumption Scheduling Research Articles

Big Data Energy Scheduling Game Management Algorithm Based on Dual Carbon Goals

“Carbon peak and neutrality” are an important strategic decision to promote the transformation of China’s energy economy and build a community with a shared future for mankind. China is a big energy consumer, and the whole society is facing huge challenges under the goal of carbon peak and neutrality. The realization of the goal of carbon peak and neutrality requires the guidance of correct theoretical methods and scientific deployment. This paper mainly studies the energy consumption scheduling, demand response management, and energy trading problems of microgrid. In view of the shortcomings of the existing energy optimization scheduling methods in the microgrid, a variety of energy resources such as electric energy, natural gas, heat energy, and cold energy are considered into the microgrid model. Based on the noncooperative game and Stackelberg game, a new energy optimal dispatch model is constructed with a variety of game methods such as two-layer game. Maximize the personal benefits of the microgrid while meeting the reliable operation of the system and the electricity demand of users. The three-stage noncooperative game problem is solved based on the reverse bootstrap method, and the closed expression of the optimal strategy in each stage is obtained. The power generation forecasting technology based on big data is studied, and a power forecasting method is proposed, which can effectively guide the energy consumption of the microgrid. The simulation results show the effectiveness of the proposed renewable energy management model based on big data, which verifies that the accurate wind power prediction results are conducive to better theoretical analysis of energy management.

Auction-Based Scheduling of Excess Energy Consumption to Enhance Grid Upward Flexibility

Auction-Based Scheduling of Excess Energy Consumption to Enhance Grid Upward Flexibility

Research on optimal scheduling of county main new energy consumption based on internal non-cooperative game under curve transaction

Under the “double carbon” goal and background, new energy power generation will usher in great development. The investment and construction of new energy projects and the scale of grid connected transmission are increasing, which puts forward higher requirements for the investment decision-making management and income of county new energy subjects. Based on the power curve trading and non cooperative game theory and the day ahead real-time two-stage adjustment and correction mechanism of county subjects, this paper briefly analyzes the new energy consumption and income of county subjects based on internal non cooperative game under curve trading.

Retracted] Dynamic Combined Optimal Scheduling of Electric Energy and Natural Gas Energy Consumption in Data Center

Energy consumption is the key factor leading to high operating costs in data center. In this paper, considering the data center service level under the premise of using electricity and gas as energy supply of energy supply and energy consumption from two angles, the data center energy scheduling model is built. The lower model is calculation scheduling model for the data center, and the upper model is data center energy supply scheduling model. Then the particle swarm algorithm is used to simulate the schedule. The results show that using natural gas as supplementary energy supply can effectively reduce the overall energy consumption of data center in considering the service delay level of data center.

Optimized Cognitive Learning Model for Energy Efficient Fog-BAN-IoT Networks

In Internet of Things (IoT), large amount of data are processed and communicated through different network technologies. Wireless Body Area Networks (WBAN) plays pivotal role in the health care domain with an integration of IoT and Artificial Intelligence (AI). The amalgamation of above mentioned tools has taken the new peak in terms of diagnosis and treatment process especially in the pandemic period. But the real challenges such as low latency, energy consumption high throughput still remains in the dark side of the research. This paper proposes a novel optimized cognitive learning based BAN model based on Fog-IoT technology as a real-time health monitoring systems with the increased network-life time. Energy and latency aware features of BAN have been extracted and used to train the proposed fog based learning algorithm to achieve low energy consumption and low-latency scheduling algorithm. To test the proposed network, Fog-IoT-BAN test bed has been developed with the battery driven MICOTT boards interfaced with the health care sensors using Micro Python programming. The extensive experimentation is carried out using the above test beds and various parameters such as accuracy, precision, recall, F1score and specificity has been calculated along with QoS (quality of service) parameters such as latency, energy and throughput. To prove the superiority of the proposed framework, the performance of the proposed learning based framework has been compared with the other state-of-art classical learning frameworks and other existing Fog-BAN networks such as WORN, DARE, L-No-DEAF networks. Results proves the proposed framework has outperformed the other classical learning models in terms of accuracy and high False Alarm Rate (FAR), energy efficiency and latency.

Distributionally robust energy consumption scheduling of HVAC considering the uncertainty of outdoor temperature and human activities

Distributionally robust energy consumption scheduling of HVAC considering the uncertainty of outdoor temperature and human activities

A Bayesian Game Approach for Demand Response Management Considering Incomplete Information

Residential flexible resource is attracting much attention in demand response (DR) for peak load shifting. This paper proposes a scenario for multi-stage DR project to schedule energy consumption of residential communities considering the incomplete information. Communities in the scenario can decide whether to participate in DR in each stage, but the decision is the private information that is unknown to other communities. To optimize the energy consumption, a Bayesian game approach is formulated, in which the probability characteristic of the decision-making of residential communities is described with Markov chain considering human behavior of bounded rationality. Simulation results show that the proposed approach can benefit all residential communities and power grid, but the optimization effect is slightly inferior to that in complete information game approach.

Optimal Design of Battery Swapping-Based Electrified Public Bus Transit Systems

This article proposes a novel model to optimize the design of fully electrified public bus transit (PBT) systems that are operating using the concept of battery swapping. The proposed optimization model is formulated as a multiobjective mixed-integer nonlinear programming model that aims at minimizing the overall capital and operation expenditures of the electrified bus transit system. The formulated model determines the optimal configuration design parameters of the electrified bus transit system, including capacity of the onboard electric bus batteries, rated power of chargers, and the number of installed chargers and battery modules at the battery swapping station (BSS). Also, the model yields the optimal schedules of batteries swapping and charging scheme of batteries at the BSS. The model takes into consideration several physical and operation constraints, such as the limits of batteries state of charge, dynamic changes in the electricity prices, impacts of traffic conditions and heat, ventilation, and air conditioning (HVAC) operation on the battery electric bus (BEB) energy consumption, stiff and flexible schedule of bus assignments, and impacts of bus transit rush hour periods on batteries swapping. Several case studies are carried out on a real PBT system in the province of Ontario, Canada, to validate the effectiveness of the proposed model. The proposed model could be utilized as a decision-making tool to investigate the applicability of using the battery swapping concept to electrify bus transits based on the operation requirements and preferences of their operators.

Integration of deterministic and game-based energy consumption scheduling for demand side management in isolated microgrids

Abstract In this work, a group Autonomous Demand-Side Management (ADSM) program for day-ahead scheduling of energy consumption profiles in an isolated microgrid is proposed, aiming to reduce the overall energy generation cost. The proposed program is applied to a Multiple-Sources Multiple-Customers (MSMC) system, consisting of a shared centralized energy source, distributed Renewable Energy Sources (RESs) and Distributed Storage Elements (DSEs). The proposed program integrates a Deterministic Energy Management (DEM) strategy with a Tabu Search (TS)-based scheduling game to reduce the computational complexity while acknowledging the intermittent nature of RESs as well as the level of customers’ comfort. Moreover, an equitable billing mechanism for charging customers based on their energy consumption and adherence to their assigned schedules is implemented. Simulations of the proposed program reveal that the TS-based algorithm offers similar energy generation costs and peak-to-average energy ratio (PAER) to those obtained with a benchmark Branch and Bound (BB) algorithm, with a significantly lower computational complexity, while being superior in computational complexity, energy costs and PAER to an algorithm based on Parallel Monte Carlo Tree Search (P-MCTS). Furthermore, the proposed integrated DEM and TS-based scheduling strategy in an MSMC system is demonstrated to offer 48% reduction in the one-day energy generation costs, compared to a SSMC system managed using a TS-based scheduling game alone.

Online Green Communication Scheduling for Sliced Unlicensed Heterogeneous Networks

New radio in unlicensed spectrum (NR-U) and network slicing have been emerged as two promising technologies to meet with the demand of enormous traffic volume, extreme transmission speed, and highly heterogeneous service requirements in future 5 G networks. Additionally, spurred by both economic considerations and environmental concerns, green communication becomes a new prominent figure of merit in the design of future 5 G networks. In this paper, we investigate an energy consumption scheduling for unlicensed heterogeneous networks, namely NR-U and WiFi, by leveraging the network slicing technology. First, we introduce a network slice broker, which enables virtual heterogeneous network operators to request and lease resources cooperatively via signaling means. Then, we investigate a joint network selection and resource allocation scheduling to minimize the network energy consumption in real time environment, taking into account various user data rate and delay requirements. An online Lyapunov optimization algorithm has been introduced to solve the proposed problem with low computational complexity and rapid convergence. We show that there exists a tradeoff between energy consumption and traffic delay, which can be adjusted through control parameters. Numerical results are presented to confirm our analyses and demonstrate the effectiveness of the proposed algorithms.

Design and Implementation of Multi-Source and Multi-Consumer Energy Sharing System in Collaborative Smart Microgrid Installation

Many published studies debated electrical energy management. They mainly investigate the multi-source installation to develop energy efficiency during its different phases: production, distribution, and consumption. Although it is rarely discussed, energy sharing is a critical part of the energy management system. In this contribution, a demand-side management algorithm is developed, that incorporates energy consumption scheduler capacity. It provides optimal energy sharing, counting on suitable energy cost parameters and adequate multi-source installation. Using this proposal, the electrical bill decreases thanks to the optimal daily attribution of schedules to households formed by a multi-consumer microgrid. This application guarantees a maximal reduction of electrical cost for the set of energy partners as one prosumer used to consume and produce power. In addition, it maintains energy efficiency as it aids in avoiding breakdowns, and depressing the peak-to-average ratio. It admits that the utility company is, as usual, always reachable non-renewable source. At the same time, renewable energy was engendered by photovoltaic panels concomitant with wind turbines stations. The application is based on the JNET protocol stack. The proposed energy sharing algorithm is implemented by using Arduino board and JN5148 nodes as a star Wireless Sensors Network topology. It is installed as a prototype in the Digital Research Center of Sfax in Tunisia. This proposed incentive-based algorithm managed to reduce the smart microgrid annual cost by almost 55% without harming the public utility. It can even ensure a more significant diminution by selling the surplus of renewable power at the end of each day.

Demand side management of energy consumption in a photovoltaic integrated greenhouse

With the continuous expansion of the development scale, photovoltaic integrated greenhouses are becoming an important type of smart grid prosumer. The demand side management (DSM) of energy is becoming increasingly important in terms of satisfying the energy consumption requirements of production and increasing the economic benefits of the same piece of land. By analyzing greenhouse production activities and the operation of electrical equipment, a particle swarm optimization (PSO) scheme is proposed in this paper, which selects the starting time of time-shifting powers as optimization factors and maximizes the income of power generation as the objective function. In addition, this paper analyzes the problem of energy consumption scheduling in greenhouse production. The results show that this scheme helps to optimally schedule the greenhouse energy consumption and achieves maximum profit by ensuring the reasonable operation of the system. The optimization scheme is beneficial to guide the production activities in greenhouses and the construction of photovoltaic integrated greenhouse systems.

Reinforcement Learning Based Peer-to-Peer Energy Trade Management Using Community Energy Storage in Local Energy Market

Many studies have proposed a peer-to-peer energy market where the prosumers’ actions, including energy consumption, charge and discharge schedule of energy storage systems, and transactions in local energy markets, are controlled by a central operator. In this paper, prosumers’ actions are not controlled by an operator, and the prosumers freely participate in the local energy market to trade energy with other prosumers. We designed and modeled a local energy market with a management algorithm that uses community energy storage for prosumers who competitively participate in trade in the real-time energy market. We propose an energy-trade management algorithm that manages the trades of prosumers in two phases based on bids and offers submitted by prosumers. The first phase is to manage the trade of prosumers who have submitted fair prices to trade with other prosumers in the real-time energy market. The second phase is managing the trade of prosumers that could not trade in the first phase. Community energy storage is employed in the second phase and controlled by a reinforcement learning-based trading algorithm to decide whether to buy, sell, or do nothing with the prosumers. The action of buying and selling means charging and discharging the community energy storage, respectively. Numerical results show that the proposed trading algorithm gains a near-maximum profit. Besides, we verified that community energy storage yields more profit than the battery wear-out cost.

An Efficient Dynamic-Decision Based Task Scheduler for Task Offloading Optimization and Energy Management in Mobile Cloud Computing.

Restricted abilities of mobile devices in terms of storage, computation, time, energy supply, and transmission causes issues related to energy optimization and time management while processing tasks on mobile phones. This issue pertains to multifarious mobile device-related dimensions, including mobile cloud computing, fog computing, and edge computing. On the contrary, mobile devices’ dearth of storage and processing power originates several issues for optimal energy and time management. These problems intensify the process of task retaining and offloading on mobile devices. This paper presents a novel task scheduling algorithm that addresses energy consumption and time execution by proposing an energy-efficient dynamic decision-based method. The proposed model quickly adapts to the cloud computing tasks and energy and time computation of mobile devices. Furthermore, we present a novel task scheduling server that performs the offloading computation process on the cloud, enhancing the mobile device’s decision-making ability and computational performance during task offloading. The process of task scheduling harnesses the proposed empirical algorithm. The outcomes of this study enable effective task scheduling wherein energy consumption and task scheduling reduces significantly.

Pipe-lining dynamic programming processes to synchronize both the production and the consumption of energy

Synchronizing heterogeneous processes remains a difficult issue in Scheduling area. Related ILP models are in trouble, because of large gaps induced by rational relaxation. We choose here to deal with it while emulating the interactions which take place between the various players of such heterogeneous processes, and propose a pipe-line decomposition of a dynamic programming process designed in order to schedule energy production and energy consumption

DroneCOCoNet: Learning-based edge computation offloading and control networking for drone video analytics

Multi-Unmanned Aerial Vehicle (UAV) systems with high-resolution cameras have been found useful for operations such as smart city and disaster management. These systems feature Flying Ad-Hoc Networks (FANETs) that connect the computation edge with UAVs and a Ground Control Station (GCS) through air-to-ground wireless network links. Leveraging the edge/fog computation resources effectively with energy-latency-awareness, and handling intermittent failures of FANETs are the major challenges in supporting video processing applications. In this paper, we propose a novel “DroneCOCoNet” framework for drone video analytics that coordinates intelligent processing of large video datasets using edge computation offloading and performs network protocol selection based on resource-awareness. We present two edge computation offloading approaches, i.e., heuristic-based and reinforcement learning-based approaches. These approaches provide intelligent task sharing and co-ordination for dynamic offloading decision-making among UAVs. Our scheme handles the problem of computation offloading tasks in two separate ways: (i) heuristic decision-making process, and (ii) Markov decision process; wherein we aim to minimize the total computation costs as well as latency in the edge/fog resources while minimizing video processing times to meet application requirements. Our experimental results show that our heuristic-based offloading decision-making scheme enables lower scheduling time and energy consumption for low drone-to-ground server ratios. In comparison, our dynamic reinforcement learning-based decision-making approach increases the accuracy and saves overall time periodically. Notably, these results also hold in various other multi-UAV scenarios involving largely different numbers of detected objects in e.g., smart farming, transportation traffic flow monitoring and disaster response.

A Comprehensive Review on Residential Demand Side Management Strategies in Smart Grid Environment

The ever increasing demand for electricity and the rapid increase in the number of automatic electrical appliances have posed a critical energy management challenge for both utilities and consumers. Substantial work has been reported on the Home Energy Management System (HEMS) but to the best of our knowledge, there is no single review highlighting all recent and past developments on Demand Side Management (DSM) and HEMS altogether. The purpose of each study is to raise user comfort, load scheduling, energy minimization, or economic dispatch problem. Researchers have proposed different soft computing and optimization techniques to address the challenge, but still it seems to be a pressing issue. This paper presents a comprehensive review of research on DSM strategies to identify the challenging perspectives for future study. We have described DSM strategies, their deployment and communication technologies. The application of soft computing techniques such as Fuzzy Logic (FL), Artificial Neural Network (ANN), and Evolutionary Computation (EC) is discussed to deal with energy consumption minimization and scheduling problems. Different optimization-based DSM approaches are also reviewed. We have also reviewed the practical aspects of DSM implementation for smart energy management.

Economical Speed for Optimizing the Travel Time and Energy Consumption in Train Scheduling using a Fuzzy Multi-Objective Model

Speed is one of the most influential variables in both energy consumption and train scheduling problems. Increasing speed guarantees punctuality, thereby improving railroad capacity and railway stakeholders’ satisfaction and revenues. However, a rise in speed leads to more energy consumption, costs, and thus, more pollutant emissions. Therefore, determining an economic speed, which requires a trade-off between the user’s expectations and the capabilities of the railway system in providing tractive forces to overcome the running resistance due to rail route and moving conditions, is a critical challenge in railway studies. This paper proposes a new fuzzy multi-objective model, which, by integrating micro and macro levels and determining the economical speed for trains in block sections, can optimize train travel time and energy consumption. Implementing the proposed model in a real case with different scenarios for train scheduling reveals that this model can enhance the total travel time by 19% without changing the energy consumption ratio. The proposed model has little need for input from experts’ opinions to determine the rates and parameters.

Adaptive energy consumption scheduling of multi-microgrid using whale optimization algorithm

In this paper, we investigated the day-ahead scheduling of network consisting of several distributed generation sources and connected loads. This scheduling is an optimization problem that has two objective functions, namely, operating costs and the cost caused by the emission of pollutants. In this scheduling, while introducing a new structure for micro-grid utilization, the possibility of exchanging power among networks and exchanging power between networks and the upstream distribution network is provided. The uncertainty of distributed wind generation sources and solar units has been investigated using appropriate probabilistic distributions. Incent-driven load-responsiveness management programs have been used as to encourage subscribers to reduce their power consumption during high-load hours. Whale optimization algorithm has been used for scheduling of network.

LEVEL OF ENERGY SUPPLY OF ANIMAL FARMS FROM USING BIOGAS

Ukraine has untapped potential for the production of own energy from renewable sources - processing of livestock waste (animal manure and bird droppings) with the formation of biogas, which can then be used to produce electricity, heat or fuel - analogues of natural gas (methane) (in particular, for own needs of farms). Currently in Ukraine, the generation of large amounts of waste on industrial farms is an environmental problem that needs to be addressed. Recycling livestock waste to generate biogas will partially solve environmental problems, as well as benefit from decentralized renewable energy production or fuel production. The role of renewable energy sources in energy production is constantly growing and now the issue of increasing the share of renewable sources in the energy balance of each country is relevant. In the supply of primary energy, the share of renewable energy accounts for 13% worldwide. Of these, biomass accounts for 10%, or 258 million tons per year, ie in the world biomass provides the largest share of energy supply from renewable sources. Domestic agricultural enterprises are significant consumers of fuel and energy resources, so they face the objective need to use alternative energy sources, including biofuels and the introduction of innovative energy-saving technologies. Biogas production is an efficient and attractive investment technology, due to the presence of significant raw material potential, favorable climatic and other. However, the level of introduction of this type of energy in the agro-industrial complex is insufficient, which is due to a number of issues, including insufficient level of practical recommendations for choosing power generators, their number, modes of operation taking into account daily energy consumption schedule, load and optimal efficiency. installations while providing autonomous power supply of the enterprise. This paper determines the level of energy supply of livestock farms through the use of biogas plants as a source of energy used for livestock waste.