An Optimal Dynamic Generation Scheduling for a Wind-Thermal Power System

In this paper, a dynamic generation scheduling model is formulated, aiming at minimizing the costs of power generation and taking into account the constraints of thermal power units and spinning reserve in wind power integrated systems. A dynamic solving method blended with particle swarm optimization algorithm is proposed. In this method, the solution space of the states of unit commitment is created and will be updated when the status of unit commitment changes in a period to meet the spinning reserve demand. The thermal unit operation constrains are inspected in adjacent time intervals to ensure all the states in the solution space effective. The particle swarm algorithm is applied in the procedure to optimize the load distribution of each unit commitment state. A case study in a simulation system is finally given to verify the feasibility and effectiveness of this dynamic optimization algorithm.

Short-term power generation scheduling rules for cascade hydropower stations based on hybrid algorithm

Power generation dispatching is a large complex system problem with multi-dimensional and nonlinear characteristics. A mathematical model was established based on the principle of reservoir operation. A large quantity of optimal scheduling processes were obtained by calculating the daily runoff process within three typical years, and a large number of simulated daily runoff processes were obtained using the progressive optimality algorithm （POA） in combination with the genetic algorithm （GA）. After analyzing the optimal scheduling processes, the corresponding scheduling rules were determined, and the practical formulas were obtained. These rules can make full use of the rolling runoff forecast and carry out the rolling scheduling. Compared with the optimized results, the maximum relative difference of the annual power generation obtained by the scheduling rules is no more than 1%. The effectiveness and practical applicability of the scheduling rules are demonstrated by a case study. This study provides a new perspective for formulating the rules of power generation dispatching.

Learning to branch in the generation maintenance scheduling problem

To maximize the reliability index of a power system,this study modeled a generation maintenance scheduling problem that considers the network security constraints and rationality constraints of the generation maintenance practice in a power system.In view of the computational complexity of the generation maintenance scheduling model,a variable selection method based on a support vector machine(SVM)is proposed to solve the 0-1 mixed integer programming problem(MIP).The algorithm observes and collects data from the decisions made by strong branching(SB)and then learns a surrogate function that mimics the SB strategy using a support vector machine.The learned ranking function is then used for variable branching during the solution process of the model.The test case showed that the proposed variable selection algorithm-based on the features of the proposed generation maintenance scheduling problem during branch-and-bound-can increase the solution efficiency of the generation-scheduling model on the premise of guaranteed accuracy.

Physics-informed neural network approach for heat generation rate estimation of lithium-ion battery under various driving conditions

Accurate insight into the heat generation rate(HGR) of lithium-ion batteries(LIBs) is one of key issues for battery management systems to formulate thermal safety warning strategies in advance.For this reason,this paper proposes a novel physics-informed neural network(PINN) approach for HGR estimation of LIBs under various driving conditions.Specifically,a single particle model with thermodynamics(SPMT) is first constructed for extracting the critical physical knowledge related with battery HGR.Subsequently,the surface concentrations of positive and negative electrodes in battery SPMT model are integrated into the bidirectional long short-term memory(BiLSTM) networks as physical information.And combined with other feature variables,a novel PINN approach to achieve HGR estimation of LIBs with higher accuracy is constituted.Additionally,some critical hyperparameters of BiLSTM used in PINN approach are determined through Bayesian optimization algorithm(BOA) and the results of BOA-based BiLSTM are compared with other traditional BiLSTM/LSTM networks.Eventually,combined with the HGR data generated from the validated virtual battery,it is proved that the proposed approach can well predict the battery HGR under the dynamic stress test(DST) and worldwide light vehicles test procedure(WLTP),the mean absolute error under DST is 0.542 kW/m^(3),and the root mean square error under WLTP is1.428 kW/m^(3)at 25℃.Lastly,the investigation results of this paper also show a new perspective in the application of the PINN approach in battery HGR estimation.

Power generation expansion planning approach considering carbon emission constraints

Decarbonization of the power sector in China is an essential aspect of the energy transition process to achieve carbon neutrality.The power sector accounts for approximately 40%of China’s total CO_(2) emissions.Accordingly,collaborative optimization in power generation expansion planning(GEP)simultaneously considering economic,environmental,and technological concerns as carbon emissions is necessary.This paper proposes a collaborative mixedinteger linear programming optimization approach for GEP.This minimizes the power system’s operating cost to resolve emission concerns considering energy development strategies,flexible generation,and resource limitations constraints.This research further analyzes the advantages and disadvantages of current GEP techniques.Results show that the main determinants of new investment decisions are carbon emissions,reserve margins,resource availability,fuel consumption,and fuel price.The proposed optimization method is simulated and validated based on China’s power system data.Finally,this study provides policy recommendations on the flexible management of traditional power sources,the market-oriented mechanism of new energy sources,and the integration of new technology to support the attainment of carbon-neutral targets in the current energy transition process.

Six Generation Load Cells Solution Based Congestion Management Control Purpose

Several assessing efforts are approved making both communication and information technologies available.In addition to being a part of universal access and service,we aim at assessing the impact of radio access technologies on universal access indicators.The proposed work demonstrates the possibility of inter-working of all existing Radio Access Technologies(RATs),in a limited area presented in the Six Generation Radio Resources Allocation(6 G-A)network.We propose a solution for the Vertical hand Over(VHO)in 6 G-A heterogeneous system,that adopts Media Independent Handover(MIH)protocol to benefit between different used technologies in a direct communication firstly and facilitate,in addition,coordination between congestion controlmechanisms with themobilitymanagement entity.The mathematical model has been developed to deal with locality coverage and broadband needs,based on a methodological approach consisting in integrating parameters linked to access index through radio technologies.This approach has been applied.In practice,it has contributed to highlighting its relative simplicity of implementation,but it is not enough for sixgeneration system radio access networks.The congestion control mechanism is integrated within the vertical handover process,this proposition is used to prevent each presented congestion state.The media independent handover IS equipment,as well as six G-A equipment which are both considered as the whole architecture key equipment,perform and guaranty this related process.Efficient communication is established besides information exchange between various technologies.Achieved evaluation results are performant and prove that proposed mechanisms are efficient.Both simulation and tests results are accomplished.

Non-dominated sorting culture differential evolution algorithm for multi-objective optimal operation of Wind-Solar-Hydro complementary power generation system

Due to the intermittency and instability of Wind-Solar energy and easy compensation of hydropower, this study proposes a Wind-Solar-Hydro power optimal scheduling model. This model is aimed at maximizing the total system power generation and the minimum ten-day joint output. To effectively optimize the multi-objective model, a new algorithm named non-dominated sorting culture differential evolution algorithm(NSCDE) is proposed. The feasibility of NSCDE was verified through several well-known benchmark problems. It was then applied to the Jinping Wind-Solar-Hydro complementary power generation system. The results demonstrate that NSCDE can provide decision makers a series of optimized scheduling schemes.

Performance Evaluation of Scheduling Algorithms for 4G (LTE)

Long Term Evolution (LTE) is designed to revolutionize mobile broadband technology with key considerations of higher data rate, improved power efficiency, low latency and better quality of service. This work analyzes the impact of resource scheduling algorithms on the performance of LTE (4G) and WCDMA (3G) networks. In this paper, a full illustration of LTE system is given together with different scheduling algorithms. Thereafter, 3G WCDMA and 4G LTE networks were simulated using Simulink simulator embedded in MATLAB and performance evaluations were carried out. The performance metrics used for the evaluations are average system throughput, packet delay, latency and allocation of fairness using Round Robin, Best CQI and Proportional fair Packet Scheduling Algorithms. The results of the evaluations on both networks were analysed. The results showed that 4G LTE network performs better than 3G WCDMA network in all the three scheduling algorithms used.

Deep Cutting Plane Inequalities for Stochastic Non-Preemptive Single Machine Scheduling Problem

We study the classical single machine scheduling problem but with uncertainty. A robust optimization model is presented, and an effective deep cut is derived. Numerical experiments show effectiveness of the derived cut.

Economic Scheduling of Gaseous-liquid Hydrogen Generation and Storage Plants Considering Complementarity of Multiple Products

The accessible and convenient hydrogen supply is the foundation of successful materialization for hydrogen-powered vehicles(HVs).This paper proposes a novel optimal scheduling model for gaseous-liquid hydrogen generation and storage plants powered by renewable energy to enhance the economic feasibility of investment.The gaseous-liquid hydrogen generation and storage plant can be regarded as an energy hub to supply concurrent service to both the transportation sector and ancillary market.In the proposed model,the power to multi-state hydrogen(P2MH)process is analyzed in detail to model the branched hydrogen flow constraints and the corresponding energy conversion relationship during hydrogen generation,processing,and storage.To model the coupling and interaction of diverse modules in the system,the multi-energy coupling matrix is developed,which can exhibit the mapping of power from the input to the output.Based on this,a multi-product optimal scheduling(MPOS)algorithm considering complementarity of different hydrogen products is further formulated to optimize dispatch factors of the energy hub system to maximize the profit within limited resources.The demand response signals are incorporated in the algorithm to further enhance the operation revenue and the scenario-based method is deployed to consider the uncertainty.The proposed methodology has been fully tested and the results demonstrate that the proposed MPOS can lead to a higher rate of return for the gaseous-liquid hydrogen generation and storage plant.

A branch and price algorithm for the robust WSOS scheduling problem

To analyze and optimize the weapon system of systems(WSOS)scheduling process,a new method based on robust capabilities for WSOS scheduling optimization is proposed.First,we present an activity network to represent the military mission.The member systems need to be reasonably assigned to perform different activities in the mission.Then we express the problem as a set partitioning formulation with novel columns(activity flows).A heuristic branch-and-price algorithm is designed based on the model of the WSOS scheduling problem(WSOSSP).The algorithm uses the shortest resource-constrained path planning to generate robust activity flows that meet the capability requirements.Finally,we discuss this method in several test cases.The results show that the solution can reduce the makespan of the mission remarkably.

A Novel Particle Swarm Optimization for Optimal Scheduling of Hydrothermal System

A fuzzy adaptive particle swarm optimization (FAPSO) is presented to determine the optimal operation of hydrothermal power system. In order to solve the shortcoming premature and easily local optimum of the standard particle swarm optimization (PSO), the fuzzy adaptive criterion is applied for inertia weight based on the evolution speed factor and square deviation of fitness for the swarm, in each iteration process, the inertia weight is dynamically changed using the fuzzy rules to adapt to nonlinear optimization process. The performance of FAPSO is demonstrated on hydrothermal system comprising 1 thermal unit and 4 hydro plants, the comparison is drawn in PSO, FAPSO and genetic algorithms (GA) in terms of the solution quality and computational efficiency. The experiment showed that the proposed approach has higher quality solutions and strong ability in global search.

Research on the Model of Long Term Generation Planning in Power Market Reform

With the deepening of China’s power market, bilateral transactions will continue to grow in large scale. The release of bilateral transactions locked more regulatory resources of the power grid, will directly affect the operation mode of the unit and the implementation of planned electricity. In the paper, considering the large-scale bilateral trade effect on the peak regulation of power grid, energy saving and emission reduction, power system security and other factors, and then putting forward the method of long term generation planning and annual planning model to adapt to the safe operation of power grid in China. In the model, the target is minimizing the monthly load rate deviation and the annual electric quantity deviation rate, the latter includes the capacity factor. In addition, the constraints include the monthly quantity of electricity, adjustable utilization rate deviation, load rate, reserve and key sections, etc. Through an example to verify the correctness of the model, the planning and power transaction results can satisfy the peak regulation of load, energy saving and emission reduction and safety operation of the power grid requirements.

Integrated Frequency-constrained Scheduling Considering Coordination of Frequency Regulation Capabilities from Multi-source Converters

As the proportion of renewable energy(RE)increases,the inertia and the primary frequency regulation(FR)capability of the power system decrease.Thus,ensuring frequency security in the scheduling model has become a new technical requirement in power systems with a high share of RE.Due to a shortage of conventional synchronous generators,the frequency support of multi-source converters has become an indispensable part of the system frequency resources,especially variable-speed wind turbine generation(WTG)and battery energy storage(BES).Quantitative expression of the FR capability of multi-source converters is necessary to construct frequency-constrained scheduling model.However,the frequency support performance of these converter-interfaced devices is related to their working states,operation modes,and parameters,and the complex coupling of these factors has not been fully exploited in existing models.In this study,we propose an integrated frequency-constrained scheduling model considering the coordination of FR capabilities from multi-source converters.Switchable FR control strategies and variable FR parameters for WTG with or without reserved power are modeled,and multi-target allocation of BES capacity between tracking dispatch instruction and emergency FR is analyzed.Then,the variable FR capabilities of WTG and BES are embedded into the integrated frequency-constrained scheduling model.The nonlinear constraints for frequency security are precisely linearized through an improved iteration-based strategy.The effectiveness of the proposed model is verified in a modified IEEE 24-bus standard system.The results suggest that the coordinated participation of BES and WTG in FR can effectively reduce the cost of the scheduling model while meeting frequency security constraints.

Robust coordination of interdependent electricity and natural gas systems in day-ahead scheduling for facilitating volatile renewable generations via power-to-gas technology

The increasing interdependency of electricity and natural gas systems promotes coordination of the two systems for ensuring operational security and economics.This paper proposes a robust day-ahead scheduling model for the optimal coordinated operation of integrated energy systems while considering key uncertainties of the power system and natural gas system operation cost. Energy hub,with collocated gas-fired units, power-to-gas(Pt G) facilities, and natural gas storages, is considered to store or convert one type of energy(i.e., electricity or natural gas)into the other form, which could analogously function as large-scale electrical energy storages. The column-andconstraint generation(C&CG) is adopted to solve the proposed integrated robust model, in which nonlinear natural gas network constraints are reformulated via a set of linear constraints. Numerical experiments signify the effectiveness of the proposed model for handling volatile electrical loads and renewable generations via the coordinated scheduling of electricity and natural gas systems.

Generation maintenance scheduling based on multiple objectives and their relationship analysis

In a market environment of power systems, each producer pursues its maximal profit while the independent system operator is in charge of the system reliability and the minimization of the total generation cost when generating the generation maintenance scheduling(GMS). Thus, the GMS is inherently a multi-objective optimization problem as its objectives usually conflict with each other. This paper proposes a multi-objective GMS model in a market environment which includes three types of objectives, i.e., each producer's profit, the system reliability, and the total generation cost. The GMS model has been solved by the group search optimizer with multiple producers(GSOMP) on two test systems. The simulation results show that the model is well solved by the GSOMP with a set of evenly distributed Pareto-optimal solutions obtained. The simulation results also illustrate that one producer's profit conflicts with another one's, that the total generation cost does not conflict with the profit of the producer possessing the cheapest units while the total generation cost conflicts with the other producers' profits, and that the reliability objective conflicts with the other objectives.

Optimal generation scheduling in power system using frequency prediction through ANN under ABT environment

In a competitive and deregulated power scenario, the utilities try to maintain their real electric power generation in balance with the load demand, which creates a need for the precise real time generation scheduling （GS）. In this paper, the GS problem is solved to perform the unit commitment （UC） based on frequency prediction by using artificial neural network （ANN） with the objective to minimize the overall system cost of the state utility. The introduction of availability-based tariff （ABT） signifies the importance of frequency in GS. Under- prediction or over-prediction will result in an unnecessary commitment of generating units or buying power from central generating units at a higher cost. Therefore, an accurate frequency prediction is the first step toward optimal GS. The dependency of frequency on various parameters such as actual generation, load demand, wind power and power deficit has been considered in this paper. The proposed technique provides a reliable solution for the input parameter different from the one presented in the training data. The performance of the frequency predictor model has been evaluated based on the absolute percentage error （APE） and the mean absolute percentage error （MAPE）. The proposed predicted frequency sensitive GS model is applied to the system of Indian state of Tamilnadu, which reduces the overall system cost of the state utility by keeping off the dearer units selected based on the predicted frequency.

Consensus-based dispatch optimization of a microgrid considering meta-heuristic-based demand response scheduling and network packet loss characterization

The uncertainty inherent in power load forecasts represents a major factor in the mismatches between supply and demand in renewables-rich electricity networks, which consequently increases the energy bills and curtailed generation. As the transition to a power grid founded on the so-called grid-of-grids becomes more evident, the need for distributed control algorithms capable of handling computationally challenging problems in the energy sector does so as well. In this light, the consensus-based distributed algorithm has recently been shown to provide an effective platform for solving the complex energy management problem in microgrids. More specifically, in a microgrid context, the consensus-based distributed algorithm requires reliable information exchange with customers to achieve convergence. However, packet losses remain an important issue, which can potentially result in the failure of the overall system. In this setting, this paper introduces a novel method to effectively characterize such packet losses during information exchange between the customers and the microgrid operator, whilst solving the microgrid scheduling optimization problem for a multi-agent-based microgrid. More specifically, the proposed framework leverages the virulence optimization algorithm and the earth-worm optimization algorithm to optimally shift the energy consumption during peak periods to lower-priced off-peak hours. The effectiveness of the proposed method in minimizing the overall active power mismatches in the presence of packet losses has also been demonstrated based on benchmarking the results against the business-as-usual iterative scheduling algorithm. Also, the robustness of the overall meta-heuristic- and multi-agent-based method in producing optimal results is confirmed based on comparing the results obtained by several well-established meta-heuristic optimization algorithms, including the binary particle swarm optimization, the genetic algorithm, and the cuckoo search optimization.

Multi-level Evaluation of Load Supply Adequacy for Transmission Network

Considering sharp increase of central integration scale of intermittent energy generation, power system couldn’t rely on the determined model based operation schedule to assure its reliable load supply. Then, this article presents a new load supply adequacy evaluation method, which based on continuous security constrained generation optimization, and provides the functional architecture design of online multi-level short term load supply adequacy evaluation system.

Composite Cost Function Based Solution to the Unit Commitment Problem

This paper presents a new approach via composite cost function to solve the unit commitment problem. The unit com-mitment problem involves determining the start-up and shut-down schedules for generating units to meet the fore-casted demand at the minimum cost. The commitment schedule must satisfy the other constraints such as the generating limits, spinning reserve, minimum up and down time, ramp level and individual units. The proposed algorithm gives the committed units and economic load dispatch for each specific hour of operation. Numerical simulations were carried out using three cases: four-generator, seven-generator, and ten-generator thermal unit power systems over a 24 h period. The produced schedule was compared with several other methods, such as Dynamic programming, Branch and bound, Ant colony system, and traditional Tabu search. The result demonstrated the accuracy of the proposed method.