GRU-integrated constrained soft actor-critic learning enabled fully distributed scheduling strategy for residential virtual power plant

In this study,a novel residential virtual power plant(RVPP)scheduling method that leverages a gate recurrent unit(GRU)-integrated deep reinforcement learning(DRL)algorithm is proposed.In the proposed scheme,the GRU-integrated DRL algorithm guides the RVPP to participate effectively in both the day-ahead and real-time markets,lowering the electricity purchase costs and consumption risks for end-users.The Lagrangian relaxation technique is introduced to transform the constrained Markov decision process(CMDP)into an unconstrained optimization problem,which guarantees that the constraints are strictly satisfied without determining the penalty coefficients.Furthermore,to enhance the scalability of the constrained soft actor-critic(CSAC)-based RVPP scheduling approach,a fully distributed scheduling architecture was designed to enable plug-and-play in the residential distributed energy resources(RDER).Case studies performed on the constructed RVPP scenario validated the performance of the proposed methodology in enhancing the responsiveness of the RDER to power tariffs,balancing the supply and demand of the power grid,and ensuring customer comfort.

Optimal Scheduling Strategy of Source-Load-Storage Based onWind Power Absorption Benefit

In recent years,the proportion of installed wind power in the three north regions where wind power bases are concentrated is increasing,but the peak regulation capacity of the power grid in the three north regions of China is limited,resulting in insufficient local wind power consumption capacity.Therefore,this paper proposes a two-layer optimal scheduling strategy based on wind power consumption benefits to improve the power grid’s wind power consumption capacity.The objective of the uppermodel is tominimize the peak-valley difference of the systemload,which ismainly to optimize the system load by using the demand response resources,and to reduce the peak-valley difference of the system load to improve the peak load regulation capacity of the grid.The lower scheduling model is aimed at maximizing the system operation benefit,and the scheduling model is selected based on the rolling schedulingmethod.The load-side schedulingmodel needs to reallocate the absorbed wind power according to the response speed,absorption benefit,and curtailment penalty cost of the two DR dispatching resources.Finally,the measured data of a power grid are simulated by MATLAB,and the results show that:the proposed strategy can improve the power grid’s wind power consumption capacity and get a large wind power consumption benefit.

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.

Mid-long Term Optimal Dispatching Method of Hydro-thermal Power System Considering Scheduled Maintenance

在中长期水火发电调度中考虑检修计划的影响是目前中长期水火发电调度面临的难题。利用现代整数代数建模技术，建立发电计划和检修计划协调优化的多场景调度模型。在该模型中，鉴于设备检修计划的连续性，在预测场景树的基础上，将场景节点划分成不同的场景，通过节点和场景关联矩阵，实现多场景下设备检修模型的构建。同时，鉴于中长期调度计划中发电计划和检修计划对时段间隔要求的不同，分别设置电量相关节点和电力相关节点，实现中长期发电计划和检修计划的协调。上述模型是一个大规模混合整数线性规划（mixed integer linear programming，MILP）问题，采用商用MILP求解器进行求解。大规模实际水火电系统的实例分析结果表明，所提模型和方法是可行、有效的。

Dynamic Balance Control Based on an Adaptive Gain-scheduled Backstepping Scheme for Power-line Inspection Robots

This paper presents an adaptive gain-scheduled backstepping control(AGSBC) scheme for the balance control of an underactuated mechanical power-line inspection(PLI) robotic system with two degrees of freedom and a single control input.First, a nonlinear dynamic model of the balance adjustment process of the PLI robot is constructed, and then the model is linearized at a nominal equilibrium point to overcome the computational infeasibility of the conventional backstepping technique. Second, to solve generalized stabilization control issue for underactuated systems with multiple equilibrium points,an equilibrium manifold linearized model is developed using a scheduling variable, and then a gain-scheduled backstepping control(GSBC) scheme for expanding the operational area of the controlled system is constructed. Finally, an adaptive mechanism is proposed to counteract the impact of external disturbances. The robust stability of the closed-loop system is ensured by Lyapunov theorem. Simulation results demonstrate the effectiveness and high performance of the proposed scheme compared with other control schemes.

An Improved Simple Power Attack against Camellia's Key Schedule

This paper presents an improved simple power attack against the key schedule of Camellia. While the original attack required an exact determination of the Hamming weight of intermediate data values based on power measurements, in this paper, two types of the simple power attack are presented and shown to be tolerant of errors that might occur in the Hamming weight determinations. In practical applications of the attack, such errors are likely to occur due to noise and distortion in the power measurements and their mapping to the Hamming weights of the data. To resist these attacks, the required design rationale of key schedules and several practical countermeasures are suggested.

Schedule-Aware Power Management for Energy-Efficiency Improvement in 802.11u WLAN

Mobile stations supporting the 802.11u standard can access WLAN automatically when they are within the coverage of the network service provided by this WLAN. To achieve this goal, the stations need to keep “on” states includingidleandactiveall the time. However, studies have noted that the idleness of stations often lead to considerable power consumption. Although the conventional power saving mode (PSM) can provide energy saving effect to some extent, its own disadvantage leads to lower energy efficiency when the number of stations accessing the target WLAN. In this paper, we propose a Schedule-Aware PSM (S-PSM), which can improve the energy efficiency in 802.11u WLAN. Particularly, we use the Generic advertisement service (GAS) defined in 802.11u standard to broadcast the transmission schedule information and all stations switch off their radios based on this information accordingly. We introduce the Respond Contention Window to reduce the collision probability of competition channel. When there is no packet in the access point (AP), AP broadcasts the GAS frame and actives the Idle Timer. All stations will turn into sleep and AP will not send GAS frame until Idle Timer expires. Simulations have shown that our proposed scheme can significantly reduce power consumption compared with the conventional PSM.

Ling'ao Nuclear Power Plant is progressing as scheduled

Construction of Ling’ao Nuclear Power Plant in Guangdong Province, the country’s second-largest nuclear power project between 1996-2000, chalked up notable progress this year. On July, 15, all foreign suppliers in the project were given official notice that both sides should start to implement the contracts. "The move marked a significant step forward in the project’s construction," said Zeng Wenxing, General Manager of Ling’ ao Nuclear Power Co Ltd, set up in

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.

A Robust Reserve Scheduling Method Considering Asymmetrical Wind Power Distribution

Nowadays, limited predictability and controllability of wind power are regarded as some bottlenecks to wind generation integration with the power system. This paper introduces a robust reserve scheduling method, where the spinning reserve allocation among conventional units is considered as well.The method applies to asymmetrical wind power distribution,and offers control on the degree of solution's conservatism by changing the robustness budget. Meanwhile, distributional information of wind power is represented by mean value and asymmetrical bounds. Furthermore, the model is converted into a deterministic programming problem with dual theory. Case studies for asymmetrically distributed wind power illustrate its effectiveness.

Improved Cross Entropy Algorithm for Steelmaking Continuous Casting Production Scheduling with Minimum Power Consumption

A matrix encoding scheme for the steelmaking continuous casting( SCC) production scheduling( SCCPS) problem and the corresponding decoding method are proposed. Based on it,a cross entropy( CE) method is adopted and an improved cross entropy( ICE) algorithm is proposed to solve the SCCPS problem to minimize total power consumption. To describe the distribution of the solution space of the CE method,a probability model is built and used to generate individuals by sampling and a probability updating mechanism is introduced to trace the promising samples. For the ICE algorithm,some samples are generated by the heuristic rules for the shortest makespan due to the relation between the makespan and the total power consumption,which can reduce the search space greatly. The optimal sample in each iteration is retained through a retention mechanism to ensure that the historical optimal sample is not lost so as to improve the efficiency and global convergence. A local search procedure is carried out on a part of better samples so as to improve the local exploitation capability of the ICE algorithm and get a better result. The parameter setting is investigated by the Taguchi method of design-of-experiment. A number of simulation experiments are implemented to validate the effectiveness of the ICE algorithm in solving the SCCPS problem and also the superiority of the ICE algorithm is verified through the comparison with the standard cross entropy( SCE) algorithm.

Joint Power Control and Scheduling for Two-Cell Energy Efficient Broadcasting with Network Coding

We consider the energy minimization problem for a two-cell broadcasting system, where the focus is devising energy efficient joint power control and scheduling algorithms. To improve the retransmission efficiency, linear network coding is applied to broadcast packets. Combined with network coding, an optimal algorithm is proposed, which is based on dynamic programming. To reduce computational complexity, two sub-optimal algorithms are also proposed for large networks. Simulation results show that the proposed schemes can reduce energy consumption up to 57% compared with the traditional Automatic Repeat-reQuest (ARQ).

Cross-layer Resource Allocation on Broadband Power Line Based on Novel QoS-priority Scheduling Function in MAC Layer

Traditional resource allocation algorithms use the hierarchical system, which does not apply to the bad channel environment in broadband power line communication system. Introducing the idea of cross-layer can improve the utilization of resources and ensure the QoS of services. This paper proposes a cross-layer resource allocation on broadband power line based on QoS priority scheduling function on MAC layer. Firstly, the algorithm considers both of real-time users’ requirements for delay and non-real-time users’ requirements for queue length. And then user priority function is proposed. Then each user’s scheduled packets number is calculated according to its priority function. The scheduling sequences are based on the utility function. In physical layer, according to the scheduled packets, the algorithm allocates physical resources for packets. The simulation results show that the proposed algorithm give consideration to both latency and throughput of the system with improving users’ QoS.

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.

Interior-Point Methods Applied to the Predispatch Problem of a Hydroelectric System with Scheduled Line Manipulations

Transmission line manipulations in a power system are necessary for the execution of preventative or corrective main- tenance in a network, thus ensuring the stability of the system. In this study, primal-dual interior-point methods are used to minimize costs and losses in the generation and transmission of the predispatch active power flow in a hydroelectric system with previously scheduled line manipulations for preventative maintenance, over a period of twenty-four hours. The matrix structure of this problem and the modification that it imposes on the system is also broached in this study. From the computational standpoint, the effort required to solve a problem with or without line manipulations is similar, and the reasons for this are also discussed in this study. Computational results sustain our findings.

Energy Aware Processor Architecture for Effective Scheduling and Power Management in Cloud Using Inclusive Power-Cognizant Processor Controller

The fast acceptance of cloud technology to industry explains increasing energy conservation needs and adoption of energy aware scheduling methods to cloud. Power consumption is one of the top of mind issues in cloud, because the usage of cloud storage by the individuals or organization grows rapidly. Developing an efficient power management processor architecture has gained considerable attention. However, the conventional power management mechanism fails to consider task scheduling policies. Therefore, this work presents a novel energy aware framework for power management. The proposed system leads to the development of Inclusive Power-Cognizant Processor Controller (IPCPC) for efficient power utilization. To evaluate the performance of the proposed method, simulation experiments inputting random tasks as well as tasks collected from Google Trace Logs were conducted to validate the supremacy of IPCPC. The research based on Real world Google Trace Logs gives results that proposed framework leads to less than 9% of total power consumption per task of server which proves reduction in the overall power needed.

A new method on hydrothermal scheduling optimization in electric power market

The hydrothermal scheduling in the electric power market becomes difficult because of introducing competition and considering sorts of constraints. An augmented Lagrangian approach is adopted to solve the problem,which adds to the standard Lagrangian function a quadratic penalty term without changing its dual property,and reduces the oscillation in iterations. According to the theory of large system coordination and decomposition,the problem is divided into hydro sub-problem and thermal sub-problem,which are coordinated by updating the Lagrangian multipliers,then the optimal solution is obtained. Our results for a test system show that the augmented Lagrangian approach can make the problem converge into the optimal solution quickly.