Edge-Cloud Computing for Scheduling the Energy Consumption in Smart Grid

Nowadays,smart electricity grids are managed through advanced tools and techniques.The advent of Artificial Intelligence(AI)and network technology helps to control the energy demand.These advanced technologies can resolve common issues such as blackouts,optimal energy generation costs,and peakhours congestion.In this paper,the residential energy demand has been investigated and optimized to enhance the Quality of Service(QoS)to consumers.The energy consumption is distributed throughout the day to fulfill the demand in peak hours.Therefore,an Edge-Cloud computing-based model is proposed to schedule the energy demand with reward-based energy consumption.This model gives priority to consumer preferences while planning the operation of appliances.A distributed system using non-cooperative game theory has been designed to minimize the communication overhead between the edge nodes.Furthermore,the allotment mechanism has been designed to manage the grid appliances through the edge node.The proposed model helps to improve the latency in the grid appliances scheduling process.

A Blockchain-Based Game Approach to Multi-Microgrid Energy Dispatch

As the current global environment is deteriorating,distributed renewable energy is gradually becoming an important member of the energy internet.Blockchain,as a decentralized distributed ledger with decentralization,traceability and tamper-proof features,is an importantway to achieve efficient consumption andmulti-party supply of new energy.In this article,we establish a blockchain-based mathematical model of multiple microgrids and microgrid aggregators’revenue,consider the degree of microgrid users’preference for electricity thus increasing users’reliance on the blockchainmarket,and apply the one-master-multiple-slave Stackelberg game theory to solve the energy dispatching strategy when each market entity pursues the maximum revenue.The simulation results show that the blockchain-based dynamic game of the multi-microgrid market can effectively increase the revenue of both microgrids and aggregators and improve the utilization of renewable energy.

Dual-radiation-chamber coordinated overall energy efficiency scheduling solution for ethylene cracking process regarding multi-parameter setting and multi-flow allocation

Ethylene cracking process is the core production process in ethylene industry,and is paid more attention to reduce high energy consumption.Because of the interdependent relationships between multi-flow allocation and multi-parameter setting in cracking process,it is difficult to find the overall energy efficiency scheduling for the purpose of saving energy.The traditional scheduling solutions with optimal economic benefit are not applicable for energy efficiency scheduling issue due to the neglecting of recycle and lost energy,as well as critical operation parameters as coil outlet pressure(COP)and dilution ratio.In addition,the scheduling solutions mostly regard each cracking furnace as an elementary unit,regardless of the coordinated operation of internal dual radiation chambers(DRC).Therefore,to improve energy utilization and production operation,a novel energy efficiency scheduling solution for ethylene cracking process is proposed in this paper.Specifically,steam heat recycle and exhaust heat loss are considered in cracking process based on 6 types of extreme learning machine(ELM)based cracking models incorporating DRC operation and three operation parameters as coil outlet temperature(COT),COP,and dilution ratio according to semi-mechanism analysis.Then to provide long-term decision-making basis for energy efficiency scheduling,overall energy efficiency indexes,including overall output per unit net energy input(OONE),output-input ratio per unit net energy input(ORNE),exhaust gas heat loss ratio(EGHL),are designed based on input-output analysis in terms of material and energy flows.Finally,a multiobjective evolutionary algorithm based on decomposition(MOEA/D)is employed to solve the formulated multi-objective mixed-integer nonlinear programming(MOMINLP)model.The validities of the proposed scheduling solution are illustrated through a case study.The scheduling results demonstrate that an optimal balance between multi-flow allocation,multi-parameter setting,and DRC coordinated operation is reached,which achieves 3.37%and 2.63%decreases in net energy input for same product output and conversion ratio,as well as the 1.56%decrease in energy loss ratio.

Deep Learning Network for Energy Storage Scheduling in Power Market Environment Short-Term Load Forecasting Model

In the electricity market,fluctuations in real-time prices are unstable,and changes in short-term load are determined by many factors.By studying the timing of charging and discharging,as well as the economic benefits of energy storage in the process of participating in the power market,this paper takes energy storage scheduling as merely one factor affecting short-term power load,which affects short-term load time series along with time-of-use price,holidays,and temperature.A deep learning network is used to predict the short-term load,a convolutional neural network(CNN)is used to extract the features,and a long short-term memory(LSTM)network is used to learn the temporal characteristics of the load value,which can effectively improve prediction accuracy.Taking the load data of a certain region as an example,the CNN-LSTM prediction model is compared with the single LSTM prediction model.The experimental results show that the CNN-LSTM deep learning network with the participation of energy storage in dispatching can have high prediction accuracy for short-term power load forecasting.

Stochastic Programming for Hub Energy Management Considering Uncertainty Using Two-Point Estimate Method and Optimization Algorithm

To maximize energy profit with the participation of electricity,natural gas,and district heating networks in the day-ahead market,stochastic scheduling of energy hubs taking into account the uncertainty of photovoltaic and wind resources,has been carried out.This has been done using a new meta-heuristic algorithm,improved artificial rabbits optimization(IARO).In this study,the uncertainty of solar and wind energy sources is modeled using Hang’s two-point estimating method(TPEM).The IARO algorithm is applied to calculate the best capacity of hub energy equipment,such as solar and wind renewable energy sources,combined heat and power(CHP)systems,steamboilers,energy storage,and electric cars in the day-aheadmarket.The standard ARO algorithmis developed to mimic the foraging behavior of rabbits,and in this work,the algorithm’s effectiveness in avoiding premature convergence is improved by using the dystudynamic inertia weight technique.The proposed IARO-based scheduling framework’s performance is evaluated against that of traditional ARO,particle swarm optimization(PSO),and salp swarm algorithm(SSA).The findings show that,in comparison to previous approaches,the suggested meta-heuristic scheduling framework based on the IARO has increased energy profit in day-ahead electricity,gas,and heating markets by satisfying the operational and energy hub limitations.Additionally,the results show that TPEM approach dependability consideration decreased hub energy’s profit by 8.995%as compared to deterministic planning.

Scalable Distributed Optimization Combining Conic Projection and Linear Programming for Energy Community Scheduling

In this paper, a new method to address the scheduling problem of a renewable energy community while considering network constraints and users' privacy preservation is proposed. The method decouples the optimization solution into two interacting procedures: conic projection(CP) and linear programming(LP) optimization. A new optimal CP method is proposed based on local computations and on the calculation of the roots of a fourth-order polynomial for which a closed-form solution is known. Computational tests conducted on both 14-bus and 84-bus distribution networks demonstrate the effectiveness of the proposed method in obtaining the same quality of solutions compared with that by a centralized solver. The proposed method is scalable and has features that can be implemented on microcontrollers since both LP and CP procedures require only simple matrix-vector multiplications.

An Envy-Free Online UAV Charging Scheme with Vehicle-Mounted Mobile Wireless Chargers

In commercial unmanned aerial vehicle(UAV)applications,one of the main restrictions is UAVs’limited battery endurance when executing persistent tasks.With the mature of wireless power transfer(WPT)technologies,by leveraging ground vehicles mounted with WPT facilities on their proofs,we propose a mobile and collaborative recharging scheme for UAVs in an on-demand manner.Specifically,we first present a novel air-ground cooperative UAV recharging framework,where ground vehicles cooperatively share their idle wireless chargers to UAVs and a swarm of UAVs in the task area compete to get recharging services.Considering the mobility dynamics and energy competitions,we formulate an energy scheduling problem for UAVs and vehicles under practical constraints.A fair online auction-based solution with low complexity is also devised to allocate and price idle wireless chargers on vehicular proofs in real time.We rigorously prove that the proposed scheme is strategy-proof,envy-free,and produces stable allocation outcomes.The first property enforces that truthful bidding is the dominant strategy for participants,the second ensures that no user is better off by exchanging his allocation with another user when the auction ends,while the third guarantees the matching stability between UAVs and UGVs.Extensive simulations validate that the proposed scheme outperforms benchmarks in terms of energy allocation efficiency and UAV’s utility.

Distributionally Robust Energy Consumption Scheduling of HVAC Considering the Uncertainty of Outdoor Temperature and Human Activities

Achieving a low or zero carbon target is to reduce energy demand and improve energy efficiency of electricity consumers.One of the main electricity consumers in power systems is heating,ventilation,and air conditioning systems(HVACs),which cost around 30%of the total usage in commercial buildings.This paper investigates the scheduling problem of HVAC energy consumption taking into account two uncertainties:outdoor temperature and human activities.The distributionally robust optimization approach(DROA)is extended to deal with these two uncertainties which are modeled by the proposed disjointed layered ambiguity sets according to historical data.Based on the proposed DROA method,the distributionally robust chance constraints(DRCCs)will be formulated as a nonlinear optimization problem,converted into a linear optimization problem using duality theorem and solved using SeDuMi solver.Simulation results are used to compare with existing methods,which shows the proposed DROA can decrease 2.81%and 0.14%of the electricity cost in comparison with traditional RO method and DROA based on a nest layered ambiguity set,respectively.Also,the proposed DROA decreases the number and maximum of violations from the comfort level of users.The multi-zone HVAC system model is used in the case study to verify the proposed DROA with the disjointed ambiguity set.The consecutive simulation results illustrate the proposed DROA approach can provide stable performance in a three-day scheduling period.

STUDY ON SHIFT SCHEDULE AND SIMULATION OF AUTOMATIC TRANSMISSION

From the point of view of saving energy, a new shift schedule and auto-controlling strategy for automatic transmission are proposed. In order to verify this shift schedule, a simulation program using a software package of Matlab/ Simulink is developed. The simulation results show the shift schedule is correct. This shift schedule has enriched the theory of vehicle automatic maneuvering and will improve the efficiency of hydrodynanic drive system of the vehicle.

Secure coverage-preserving node scheduling scheme using energy prediction for wireless sensor networks

With the fast development of the micro-electro-mechanical systems （MEMS）,wireless sensor networks （WSNs） have been extensively studied.Most of the studies focus on saving energy consumption because of restricted energy supply in WSNs.Cluster-based node scheduling scheme is commonly considered as one of the most energy-efficient approaches.However,it is not always so efficient especially when there exist hot spot and network attacks in WSNs.In this article,a secure coverage-preserved node scheduling scheme for WSNs based on energy prediction is proposed in an uneven deployment environment.The scheme is comprised of an uneven clustering algorithm based on arithmetic progression,a cover set partition algorithm based on trust and a node scheduling algorithm based on energy prediction.Simulation results show that network lifetime of the scheme is 350 rounds longer than that of other scheduling algorithms.Furthermore,the scheme can keep a high network coverage ratio during the network lifetime and achieve the designed objective which makes energy dissipation of most nodes in WSNs balanced.

Reinforcement learning for whole-building HVAC control and demand response

This paper proposes a novel reinforcement learning(RL)architecture for the efficient scheduling and control of the heating,ventilation and air conditioning(HVAC)system in a commercial building while harnessing its de-mand response(DR)potentials.With advances in automated building management systems,this can be achieved seamlessly by a smart autonomous RL agent which takes the best action,for example,a change in HVAC temper-ature set point,necessary to change the electricity usage pattern of a building in response to demand response signals,and with minimal thermal comfort impact to customers.Previous research in this area has tackled only individual aspects of the problem using RL.Specifically,due to the challenges in implementing demand response with whole-building models,simpler analytical models which poorly capture reality have been used instead.And where whole-building models are applied,RL is used for HVAC control mainly to achieve energy efficiency goals while demand response is neglected.Thus,in this research,we implement a holistic framework by designing an efficient RL controller for a whole-building model which learns to optimise and control the HVAC system for improved energy efficiency and thermal comfort levels in addition to achieving demand response goals.Our simulation results show that by applying reinforcement learning for normal HVAC operation,a maximum weekly energy reduction of up to 22%can be achieved compared to a handcrafted baseline controller.Furthermore,by employing a DR-aware RL controller during demand response periods,average power reductions or increases of up to 50%can be achieved on a weekly basis compared to the default RL controller,while keeping occupant thermal comfort levels within acceptable bounds.

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.