Multi-Time Scale Optimal Scheduling of a Photovoltaic Energy Storage Building System Based on Model Predictive Control

Building emission reduction is an important way to achieve China’s carbon peaking and carbon neutrality goals.Aiming at the problem of low carbon economic operation of a photovoltaic energy storage building system,a multi-time scale optimal scheduling strategy based on model predictive control(MPC)is proposed under the consideration of load optimization.First,load optimization is achieved by controlling the charging time of electric vehicles as well as adjusting the air conditioning operation temperature,and the photovoltaic energy storage building system model is constructed to propose a day-ahead scheduling strategy with the lowest daily operation cost.Second,considering inter-day to intra-day source-load prediction error,an intraday rolling optimal scheduling strategy based on MPC is proposed that dynamically corrects the day-ahead dispatch results to stabilize system power fluctuations and promote photovoltaic consumption.Finally,taking an office building on a summer work day as an example,the effectiveness of the proposed scheduling strategy is verified.The results of the example show that the strategy reduces the total operating cost of the photovoltaic energy storage building system by 17.11%,improves the carbon emission reduction by 7.99%,and the photovoltaic consumption rate reaches 98.57%,improving the system’s low-carbon and economic performance.

Two-Stage Optimal Scheduling of Community Integrated Energy System

From the perspective of a community energy operator,a two-stage optimal scheduling model of a community integrated energy system is proposed by integrating information on controllable loads.The day-ahead scheduling analyzes whether various controllable loads participate in the optimization and investigates the impact of their responses on the operating economy of the community integrated energy system(IES)before and after;the intra-day scheduling proposes a two-stage rolling optimization model based on the day-ahead scheduling scheme,taking into account the fluctuation of wind turbine output and load within a short period of time and according to the different response rates of heat and cooling power,and solves the adjusted output of each controllable device.The simulation results show that the optimal scheduling of controllable loads effectively reduces the comprehensive operating costs of community IES;the two-stage optimal scheduling model can meet the energy demand of customers while effectively and timely suppressing the random fluctuations on both sides of the source and load during the intra-day stage,realizing the economic and smooth operation of IES.

Microgrid Optimal Scheduling

This paper presents the optimal scheduling of renewable resources using interior point optimization for grid-connected and islanded microgrids (MG) that operate with no energy storage systems. The German Jordanian University (GJU) microgrid system is used for illustration. We present analyses for islanded and grid-connected MG with no storage. The results show a feasible islanded MG with a substantial operational cost reduction. We obtain an average of $1 k daily cost savings when operating an islanded compared to a grid-connected MG with capped grid energy prices. This cost saving is 10 times higher when considering varying grid energy prices during the day. Although the PV power is intermittent during the day, the MG continues to operate with a voltage variation that does not 10%. The results imply that MGs of GJU similar topology can optimally and safely operate with no energy storage requirements but considerable renewable generation capacity.

An optimal scheduling algorithm based on task duplication

When the communication time is relatively shorter than the computation time for every task, the task duplication based scheduling (TDS) algorithm proposed by Darbha and Agrawal generates an optimal schedule. Park and Choe also proposed an extended TDS algorithm whose optimality condition is less restricted than that of TDS algorithm, but the condition is very complex and is difficult to satisfy when the number of tasks is large. An efficient algorithm is proposed whose optimality condition is less restricted and simpler than both of the algorithms, and the schedule length is also shorter than both of the algorithms. The time complexity of the proposed algorithm is O(v2), where v represents the number of tasks.

Optimal Scheduling of Electrical Energy Systems Using a Fluid Dynamic Analogy

The electricity-gas transformation problem and related intrinsic mechanisms are considered.First,existing schemes for the optimization of electricity-gas integrated energy systems are analyzed through consideration of the relevant literature,and an Electricity Hub(EH)for electricity-gas coupling is proposed.Then,the distribution mechanism in the circuit of the considered electricity-gas integrated system is analyzed.Afterward,a mathematical model for the natural gas pipeline is elaborated according to the power relationship,a node power flow calculation method,and security requirements.Next,the coupling relationship between them is implemented,and dedicated simulations are carried out.Through experimental data,it is found that after 79 data iterations,the optimization results of power generation and gas purchase cost in the new system converge to$54,936 in total,which is consistent with the data obtained by an existing centralized optimization scheme.However,the new proposed optimization scheme is found to be more flexible and convenient.

Mixed Self-adapting GA Optimal Scheduling Algorithm for a Multiple Resource Job-shop

With an aim at the job-shop scheduling problem of multiple resource constraints, this paper presents mixed self-adapting Genetic Algorithm （ GA ） , and establishes a job-shop optimal scheduling model of multiple resource constraints based on the effect of priority scheduling rules in the heuristic algorithm upon the scheduling target. New coding regulations or rules are designed. The sinusoidal function is adopted as the self-adapting factor, thus making cross probability and variable probability automatically change with group adaptability in such a way as to overcome the shortcoming in the heuristic algorithm and common GA, so that the operation efficiency is improved. The results from real example simulation and comparison with other algorithms indicate that the mixed self-adapting GA algorithm can well solve the job-shop optimal scheduling problem under the constraints of various kinds of production resources such as machine-tools and cutting tools.

Optimal Scheduling of Regional Integrated Energy Systems Considering Hybrid Demand Response

Flexible load can optimize the load curve,which is an important means to promote renewable energy consumption.The peculiarities of electricity,heat,cooling and gas loads are analyzed in this paper,considering the fuzzy degree of human perception for water temperature,and the characteristic model of hot water load is established.Considering the fuzzy degree of human perception of ambient temperature,the characteristic model of cooling load is established by using PMV and PPD index.Meanwhile,considering four combinations of cut load,translatable load,transferable load and alternative load,and considering the coupling relationship of composite parts,different response models of load are established respectively.With the minimum cost of the system,including operation and compensation costs as the objective function,the optimization scheduling model of the regional integrated energy system is established,and the Gurobi solver is used for simulation analysis to solve the optimal output and load response curve of each piece of equipment.The results show that the load curve can be optimized,the flexible regulation ability of the regional integrated energy system can be enhanced,the energy loss of the system can be reduced,and the wind power consumption ability of the system can be increased by considering the integrated demand response.

Three-Level Optimal Scheduling and Power Allocation Strategy for Power System ContainingWind-Storage Combined Unit

To mitigate the impact of wind power volatility on power system scheduling,this paper adopts the wind-storage combined unit to improve the dispatchability of wind energy.And a three-level optimal scheduling and power allocation strategy is proposed for the system containing the wind-storage combined unit.The strategy takes smoothing power output as themain objectives.The first level is the wind-storage joint scheduling,and the second and third levels carry out the unit combination optimization of thermal power and the power allocation of wind power cluster(WPC),respectively,according to the scheduling power of WPC and ESS obtained from the first level.This can ensure the stability,economy and environmental friendliness of the whole power system.Based on the roles of peak shaving-valley filling and fluctuation smoothing of the energy storage system(ESS),this paper decides the charging and discharging intervals of ESS,so that the energy storage and wind power output can be further coordinated.Considering the prediction error and the output uncertainty of wind power,the planned scheduling output of wind farms(WFs)is first optimized on a long timescale,and then the rolling correction optimization of the scheduling output of WFs is carried out on a short timescale.Finally,the effectiveness of the proposed optimal scheduling and power allocation strategy is verified through case analysis.

Deep Reinforcement Learning Based Bi-layer Optimal Scheduling for Microgrids Considering Flexible Load Control

In this paper,the bi-layer scheduling method for microgrids,based on deep reinforcement learning,is proposed to achieve economic and environmentally friendly operations.First,considering the uncertainty of renewable energy,the framework of day-ahead and intra-day scheduling is established,and the implementation scheme for both price-based and incentive-based demand response(DR)for the flexible load is determined.Then,comprehensively considering the operating characteristics of the microgrid in the day-ahead and intra-day time scales,a bi-layer scheduling model of the microgrid is established.In terms of algorithms,since day-ahead scheduling has no strict requirement for dispatching time,the particle swarm optimization(PSO)algorithm is used to optimize the time-of-use electricity price and distributed power output for the next day.Considering the environmental fluctuations and requirements for rapidity of intra-day online scheduling,the deep reinforcement learning(DRL)algorithm is adopted for optimization.Finally,based on the data from the actual microgrid,the rationality and effectiveness of the proposed scheduling method is verified.The results show that the proposed bi-layer scheduling based on the PSO and DRL algorithms achieves the optimization of scheduling cost and calculation speed,and is suitable for microgrid online scheduling.

Optimal integration of solar home systems and appliance scheduling for residential homes under severe national load shedding

In developing countries like South Africa,users experienced more than 1030 hours of load shedding outages in just the first half of 2023 due to inadequate power supply from the national grid.Residential homes that cannot afford to take actions to mitigate the challenges of load shedding are severely inconvenienced as they have to reschedule their demand involuntarily.This study presents optimal strategies to guide households in determining suitable scheduling and sizing solutions for solar home systems to mitigate the inconvenience experienced by residents due to load shedding.To start with,we predict the load shedding stages that are used as input for the optimal strategies by using the K-Nearest Neighbour(KNN)algorithm.Based on an accurate forecast of the future load shedding patterns,we formulate the residents’inconvenience and the loss of power supply probability during load shedding as the objective function.When solving the multi-objective optimisation problem,four different strategies to fight against load shedding are identified,namely(1)optimal home appliance scheduling(HAS)under load shedding;(2)optimal HAS supported by solar panels;(3)optimal HAS supported by batteries,and(4)optimal HAS supported by the solar home system with both solar panels and batteries.Among these strategies,appliance scheduling with an optimally sized 9.6 kWh battery and a 2.74 kWp panel array of five 550 Wp panels,eliminates the loss of power supply probability and reduces the inconvenience by 92%when tested under the South African load shedding cases in 2023.

Optimized scheduling of integrated energy systems for low carbon economy considering carbon transaction costs

With the introduction of the“dual carbon”goal and the continuous promotion of low-carbon development,the integrated energy system(IES)has gradually become an effective way to save energy and reduce emissions.This study proposes a low-carbon economic optimization scheduling model for an IES that considers carbon trading costs.With the goal of minimizing the total operating cost of the IES and considering the transferable and curtailable characteristics of the electric and thermal flexible loads,an optimal scheduling model of the IES that considers the cost of carbon trading and flexible loads on the user side was established.The role of flexible loads in improving the economy of an energy system was investigated using examples,and the rationality and effectiveness of the study were verified through a comparative analysis of different scenarios.The results showed that the total cost of the system in different scenarios was reduced by 18.04%,9.1%,3.35%,and 7.03%,respectively,whereas the total carbon emissions of the system were reduced by 65.28%,20.63%,3.85%,and 18.03%,respectively,when the carbon trading cost and demand-side flexible electric and thermal load responses were considered simultaneously.Flexible electrical and thermal loads did not have the same impact on the system performance.In the analyzed case,the total cost and carbon emissions of the system when only the flexible electrical load response was considered were lower than those when only the flexible thermal load response was taken into account.Photovoltaics have an excess of carbon trading credits and can profit from selling them,whereas other devices have an excess of carbon trading and need to buy carbon credits.

A deep multimodal fusion and multitasking trajectory prediction model for typhoon trajectory prediction to reduce flight scheduling cancellation

Natural events have had a significant impact on overall flight activity,and the aviation industry plays a vital role in helping society cope with the impact of these events.As one of the most impactful weather typhoon seasons appears and continues,airlines operating in threatened areas and passengers having travel plans during this time period will pay close attention to the development of tropical storms.This paper proposes a deep multimodal fusion and multitasking trajectory prediction model that can improve the reliability of typhoon trajectory prediction and reduce the quantity of flight scheduling cancellation.The deep multimodal fusion module is formed by deep fusion of the feature output by multiple submodal fusion modules,and the multitask generation module uses longitude and latitude as two related tasks for simultaneous prediction.With more dependable data accuracy,problems can be analysed rapidly and more efficiently,enabling better decision-making with a proactive versus reactive posture.When multiple modalities coexist,features can be extracted from them simultaneously to supplement each other’s information.An actual case study,the typhoon Lichma that swept China in 2019,has demonstrated that the algorithm can effectively reduce the number of unnecessary flight cancellations compared to existing flight scheduling and assist the new generation of flight scheduling systems under extreme weather.

Day-ahead optimal scheduling method for grid-connected microgrid based on energy storage control strategy

A day-ahead optimal scheduling method for a grid-connected microgrid based on energy storage(ES)control strategy is proposed in this paper.The proposed method optimally schedules ES devices to minimize the total operating costs while satisfying the load requirements of cold,heat,and electricity in microgrids.By modeling the operating cost function of each stage,the proposed method is able to adapt to different types of electricity markets and pricing mechanisms.The technical characteristics of ES,such as self-discharge and round-trip efficiency,are considered in the control strategy with a multistage process model.An improved dynamic programing method is used to solve the optimization model.Finally,case studies are provided to illustrate the application process and verify the proposed method.

Firefly algorithm with division of roles for complex optimal scheduling

A single strategy used in the firefly algorithm(FA)cannot effectively solve the complex optimal scheduling problem.Thus,we propose the FA with division of roles(DRFA).Herein,fireflies are divided into leaders,developers,and followers,while a learning strategy is assigned to each role:the leader chooses the greedy Cauchy mutation;the developer chooses two leaders randomly and uses the elite neighborhood search strategy for local development;the follower randomly selects two excellent particles for global exploration.To improve the efficiency of the fixed step size used in FA,a stepped variable step size strategy is proposed to meet different requirements of the algorithm for the step size at different stages.Role division can balance the development and exploration ability of the algorithm.The use of multiple strategies can greatly improve the versatility of the algorithm for complex optimization problems.The optimal performance of the proposed algorithm has been verified by three sets of test functions and a simulation of optimal scheduling of cascade reservoirs.

Optimal Scheduling of Distribution System with Edge Computing and Data-driven Modeling of Demand Response

High penetration of renewable energies enlarge the peak-valley difference of the net load of the distribution system,which puts forward higher requirements for the operation scheduling of the distribution system.From the perspective of leveraging demand-side adjustment capabilities,an optimal scheduling method of the distribution system with edge computing and data-driven modeling of price-based demand response(PBDR)is proposed.By introducing the edge computing paradigm,a collaborative interaction framework between the control center and the edge nodes is designed for the optimization of the distribution system.At the edge nodes,a classified XGBoost-based PBDR modeling method is proposed for large-scale differentiated users.At the control center,a two-stage optimization method integrating pre-scheduling and re-scheduling is proposed based on demand response results from all edge nodes.Through the information interaction between the control center and edge nodes,the optimized scheduling of the distribution system with large-scale users is realized.Finally,a case study is implemented on the modified IEEE 33-node system,which verifies that the proposed classified modeling method has lower errors,and it is beneficial to improve the economics of the system operation.Moreover,the simulation results show that the application of edge computing can significantly reduce the calculation time of the optimal scheduling problem with PBDR modeling of large-scale users.

Optimal scheduling of power systems considering demand response

A novel optimal scheduling method considering demand response is proposed for power systems incorporating with large scale wind power.The proposed method can jointly dispatch the energy resources and demand side resources to mitigate the fluctuation of load and wind power output.It is noticed in practical operation that,without customer’s satisfaction being considered,customers might reject the too frequent or violent demand response all together.In this case,two indices that measure the customer satisfaction are then introduced as constraints to reduce the impact to end-users and avoid extreme demand adjustment.To make the model solvable,a proximate decoupling technique is used to dispose the concave constraint introduced by the customer satisfaction constraints.Results from the case studies show that the proposed model can significantly reduce the operation cost of power system while the demand response meets customer satisfaction.Especially,the total start-up costs of conventional thermal units decreases dramatically due to less startup times.Moreover,compared to the consumption way satisfaction constraint,the payment satisfaction constraint has a heavier influence on the cost.

An Optimal Algorithm for a Class of Parallel Machines Scheduling Problem

This paper considers the parallel machines scheduling problem where jobs are subject to different release times. A constructive heuristic is first proposed to solve the problem in a modest amount of computer time. In general, the quality of the solutions provided by heuristics degrades with the increase of the probiem’s scale. Combined the global search ability of genetic algorithm, this paper proposed a hybrid heuristic to improve the quality of solutions further. The computational results show that the hybrid heuristic combines the advantages of heuristic and genetic algorithm effectively and can provide very good solutions to some large problems in a reasonable amount of computer time.

Research on Flexible Job Shop Scheduling Optimization Based on Segmented AGV

As a typical transportation tool in the intelligent manufacturing system,Automatic Guided Vehicle(AGV)plays an indispensable role in the automatic production process of the workshop.Therefore,integrating AGV resources into production scheduling has become a research hotspot.For the scheduling problem of the flexible job shop adopting segmented AGV,a dual-resource scheduling optimization mathematical model of machine tools and AGVs is established by minimizing the maximum completion time as the objective function,and an improved genetic algorithmis designed to solve the problem in this study.The algorithmdesigns a two-layer codingmethod based on process coding and machine tool coding and embeds the task allocation of AGV into the decoding process to realize the real dual resource integrated scheduling.When initializing the population,three strategies are designed to ensure the diversity of the population.In order to improve the local search ability and the quality of the solution of the genetic algorithm,three neighborhood structures are designed for variable neighborhood search.The superiority of the improved genetic algorithmand the influence of the location and number of transfer stations on scheduling results are verified in two cases.

Layered power scheduling optimization of PV hydrogen production system considering performance attenuation of PEMEL

To analyze the additional cost caused by the performance attenuation of a proton exchange membrane electrolyzer(PEMEL)under the fluctuating input of renewable energy,this study proposes an optimization method for power scheduling in hydrogen production systems under the scenario of photovoltaic(PV)electrolysis of water.First,voltage and performance attenuation models of the PEMEL are proposed,and the degradation cost of the electrolyzer under a fluctuating input is considered.Then,the calculation of the investment and operating costs of the hydrogen production system for a typical day is based on the life cycle cost.Finally,a layered power scheduling optimization method is proposed to reasonably distribute the power of the electrolyzer and energy storage system in a hydrogen production system.In the up-layer optimization,the PV power absorbed by the hydrogen production system was optimized using MALTAB+Gurobi.In low-layer optimization,the power allocation between the PEMEL and battery energy storage system(BESS)is optimized using a non-dominated sorting genetic algorithm(NSGA-Ⅱ)combined with the firefly algorithm(FA).A better optimization result,characterized by lower degradation and total costs,was obtained using the method proposed in this study.The improved algorithm can search for a better population and obtain optimization results in fewer iterations.As a calculation example,data from a PV power station in northwest China were used for optimization,and the effectiveness and rationality of the proposed optimization method were verified.

Optimal scheduling of wind-photovoltaic power-generation system based on a copula-based conditional value-at-risk model

Increasing the application of renewable energy in the power system is an effective way to achieve the goal of‘Dual Carbon’.At the same time,the high proportion of renewable energy connected to the grid endangers the safe operation of the power system.To solve this problem,this paper proposes the application of a copula function to describe the correlation between wind power and photovoltaic power,and reduce the uncertainty of power-system operation with a high proportion of renewable energy.In order to increase the robustness of the model,this paper proposes the application of the conditional value-at-risk theory to construct the objective function of the model and effectively control the tail risk of power-system operation costs.Through case analysis,it is found that the model proposed in this paper has strong practicality and economy.