Analysis and decomposition of active and reactive power spot price in deregulated electricity markets

Electricity markets have suffered important modifications in recent decades in many countries, in which a competition framework has been established with the aim of improving market efficiency and reducing energy prices. However, this new paradigm does not assure optimal solutions, as new constraints can be introduced in optimization processes that can affect the resulting prices. An example of this situation is the establishment of power purchase agreements between producers and consumers. A wide literature can be found regarding electricity markets. Some of this literature refers to the theory of spot prices and its application to them. This paper deals with the obtaining, decomposition and deduction of behavior rules of spot prices, and their influence on established contractual relationships in a deregulated market environment which allows power purchase agreements between consumers and producers. It is performed by a deterministic modeling of the complete generation-grid system. The influence of the existence of this kind of agreements on both total costs and spot prices is discussed.     

Hierarchical power management of a system with autonomously power-managed components using reinforcement learning

This paper presents a hierarchical dynamic power management (DPM) framework based on reinforcement learning (RL) technique, which aims at power savings in a computer system with multiple I/O devices running a number of heterogeneous applications. The proposed framework interacts with the CPU scheduler to perform effective application-level scheduling, thereby enabling further power savings. Moreover, it considers non-stationary workloads and differentiates between the service request generation rates of various software application. The online adaptive DPM technique consists of two layers: component-level local power manager and system-level global power manager. The component-level PM policy is pre-specified and fixed whereas the system-level PM employs temporal difference learning on semi-Markov decision process as the model-free RL technique, and it is specifically optimized for a heterogeneous application pool. Experiments show that the proposed approach considerably enhances power savings while maintaining good performance levels. In comparison with other reference systems, the proposed RL-based DPM approach, further enhances power savings, performs well under various workloads, can simultaneously consider power and performance, and achieves wide and deep power-performance tradeoff curves. Experiments conducted with multiple service providers confirm that up to 63% maximum energy saving per service provider can be achieved.     

Analysis of robust optimization for decentralized microgrid energy management under uncertainty

The present paper provides an extended analysis of a microgrid energy management framework based on Robust Optimization (RO). Uncertainties in wind power generation and energy consumption are described in the form of Prediction Intervals (PIs), estimated by a Non-dominated Sorting Genetic Algorithm (NSGA-II) – trained Neural Network (NN). The framework is tested and exemplified in a microgrid formed by a middle-size train station (TS) with integrated photovoltaic power production system (PV), an urban wind power plant (WPP) and a surrounding residential district (D). The system is described by Agent-Based Modelling (ABM): each stakeholder is modeled as an individual agent, which aims at a specific goal, either of decreasing its expenses from power purchasing or increasing its revenues from power selling. The aim of this paper is to identify which is the uncertainty level associated to the “extreme” conditions upon which robust management decisions perform better than a microgrid management based on expected values. This work shows how the probability of occurrence of some specific uncertain events, e.g., failures of electrical lines and electricity demand and price peaks, highly conditions the reliability and performance indicators of the microgrid under the two optimization approaches: (i) RO based on the PIs of the uncertain parameters and (ii) optimization based on expected values.     

Experimental study on combustion stability and performance of hydrogen-enriched compressed natural gas of a free-piston linear generator

Free Piston linear Generator (FPLG) engine fueled by compressed natural gas (CNG) has recently gained increased research attention. However, due to the low-velocity burning and poor lean limit of CNG fuel, the FPLG engine combustion stability, performance, and efficiency are still low. Hydrogen has a greater burning velocity with wider flame limits that could extend the lean burn limits and combustion characteristics of CNG. This paper compares pure CNG and 10% hydrogen-enriched CNG at various ignition speeds (0.6 ms, 0.8 m/s, and 1 m/s), injection positions (0 mm, 5 mm, 10 mm and 15 mm), and lambda ratios (0.9, 1.4 and 1.7) on the combustion characteristics, performance, and conversion efficiency are duly discussed. The findings show that the FPLG combustion stability limits increase with the hydrogen addition into the CNG. The CNG in-cylinder pressure increases significantly when the injection position is advanced, whereas the hydrogen addition reduces the influence of the injection position. The heat release rate increases by 15.62% and 23.72% with hydrogen addition, corresponding to the advanced and retarded injection positions. Consequently, the hydrogen addition increases the power RMS to 209.21 W and 232.64 W with an increment of 3.46% and 3.13%, respectively. Conclusively, the hydrogen addition into the CNG evidently shortens the combustion duration while improving the heat release rate, combustion stability, power RMS, Cycle-to-Cycle variation, and conversion efficiency.     

A new realization scheme for dynamic PFSCL style

In this paper, a new scheme of logic function realization in dynamic positive feedback source-coupled logic (D-PFSCL) style is proposed. The existing scheme implements only NOR/OR based realization of a logic function. Thus, a complex function in D-PFSCL has high gate count which degrades the overall circuit performance measured in terms of power and delay. This paper therefore aims to resolve the issue by proposing a scheme which modifies the structure of a D-PFSCL gate. The modified gate exhibits AND/OR functionality and is used to realize various functions. Simulations have been carried out by implementing various functions and comparing their performance with the existing schemes at 1 GHz. The results of performance comparison with existing schemes indicates significant reuduction in gate count resulting in overall performance improvement.     

城市轨道交通供电系统新技术研究

供电系统是轨道交通重要的子系统,城市轨道交通对供电系统要求较高。为提高城市轨道交通供电系统的技术水平和运行质量,对智能传感器、大数据、故障预测与健康管理等城市轨道交通供电系统新技术进行了研究。     

基于DSP的智能电源控制单元设计与实现

本文利用TI公司TMS320F28335芯片高效的浮点运算能力,结合片上丰富的外设,设计并实现了一种具有高可靠性的智能电源控制单元。该控制单元周期性地对各片上外设进行自检维护,完成多路负载通道控制、电压、电流的实时监控,并对故障进行指示、处理和上报,同时提供人机交互界面更新状态信息。经过验证,该控制单元工作稳定,具备良好的工程应用价值。     

面向电网实时运行风险的快速定级方法研究及应用

为满足电网运行实时风险评估时效性要求,解决电网运行实时风险评估与定级中效率与精度两者间协调的问题,在当前国家和主要电网企业规定的电网运行事故定级标准基础上提出了一种快速定级方法。快速定级方法充分考虑了电网运行实际特性,采取数据治理和算法重构两方面主要措施,对原判定方法进行优化处理。通过数据治理,大幅降低了对外部数据的依赖性,并取消了部分不必要的输入数据。采用算法重构,进一步将运行风险定级与实际业务流程紧密联系。以该快速定级方法为核心,构建了支撑电网运行风险分析与事故处置的实时运行风险管理系统,介绍了该系统的功能框架与系统架构。最后结合该系统在贵阳的实际应用情况,分析了快速定级方法的实际使用效益。     

Generation expansion planning in electricity markets: A novel framework based on dynamic stochastic MPEC

This paper presents a novel framework for generation expansion planning (GEP) of restructured power systems under uncertainty in a multi-period horizon, which includes generation investment from a price maker perspective. The investment problem is modeled as a bi-level optimization problem. The first level problem includes decisions related to investment in order to maximize total profit in the planning horizon. The second level problem consists of maximizing social welfare where the power market is cleared. Rival uncertainties on offering and investment are modeled using sets of scenarios. The bi-level optimization problem is then converted to a dynamic stochastic MPEC and represented as a mixed integer linear program (MILP) after linearization. The proposed framework is examined on a typical six-bus power network, MAZANDARAN regional electric company (MREC) transmission network as an area of IRAN interconnected power system and IEEE RTS 24-bus network. Simulation results confirm that the proposed framework can be a useful tool for analyzing the behavior of investments in electricity markets.