Smart pricing scheme: A multi-layered scoring rule application

Defining appropriate pricing strategy for smart environment is important and complex task at the same time. It holds the primal fraction in Demand Response (DR) program. In our work, we devise an incentive based smart dynamic pricing scheme for consumers facilitating a multi-layered scoring rule. The proposed strategy characterizes both incentive based DR and price based DR programs facilities. This mechanism is applied between consumer agents (CA) to electricity provider agent (EP) and EP to Generation Company (GENCO). Based on the Continuous Ranked Probability Score (CRPS), a hierarchical scoring system is formed among these entities, CA–EP–GENCO. As CA receives the dynamic day-ahead pricing signal from EP, it will schedule the household appliances to lower price period and report the prediction in a form of a probability distribution function to EP. EP, in similar way reports the aggregated demand prediction to GENCO. Finally, GENCO computes the base discount after running a cost-optimization problem. GENCO will reward EP with a fraction of discount based on their prediction accuracy. EP will do the same to CA based on how truthful they were reporting their intentions on device scheduling. The method is tested on real data provided by Ontario Power Company and we show that this scheme is capable to reduce energy consumption and consumers’ payment.     

Fast Distributed Demand Response Algorithm in Smart Grid

This paper proposes a fast distributed demand response (DR) algorithm for future smart grid based on primaldual interior method and Gaussian belief propagation (GaBP) solver. At the beginning of each time slot, each end-user/energysupplier exchanges limited rounds of messages that are not private with its neighbors, and computes the amount of energy consumption/generation locally. The proposed demand response algorithm converges rapidly to a consumption/generation decision that yields the optimal social welfare when the demands of endusers are low. When the demands are high, each end-user/energysupplier estimates its energy consumption/generation quickly such that a sub-optimal social welfare is achieved and the power system is ensured to operate within its capacity constraints. The impact of distributed computation errors on the proposed algorithm is analyzed theoretically. The simulation results show a good performance of the proposed algorithm.      

Hierarchical dispatch using two-stage optimisation for electricity markets in smart grid

This paper proposes a hierarchical dispatch method for the electricity markets consisting of wholesale markets and retail markets. In the wholesale markets, the generators and the retailers decide the generation and the purchase according to the market-clearing price. In the retail markets, the retailers set the retail price to adjust the electricity consumption of the consumers. Due to the two-way communications in smart grid, the retailers can decide the electricity purchase from the wholesale markets based on the information on electricity usage of consumers in the retail markets. We establish the hierarchical dispatch model for the wholesale markets and the retail markets and develop distributed algorithms to search for the optimal generation, purchase, and consumption. Numerical results show the balance between the supply and demand, the profits of the retailers, and the convergence of the distributed algorithms.     

移动边缘计算网络中的资源分配与定价

移动边缘计算(mobile edge computing, MEC)使移动设备(mobile device, MD)能够将任务或应用程序卸载到MEC服务器上进行处理. 由于MEC服务器在处理外部任务时消耗本地资源, 因此建立一个向 MD 收费以奖励MEC服务器的多资源定价机制非常重要. 现有的定价机制依赖于中介机构的静态定价, 任务的高度动态特性使得实现边缘云计算资源的有效利用极为困难. 为了解决这个问题, 我们提出了一个基于Stackelberg博弈的框架, 其中MEC服务器和一个聚合平台(aggregation platform, AP)充当跟随者和领导者. 我们将多重资源分配和定价问题分解为一组子问题, 其中每个子问题只考虑一种资源类型. 首先, 通过MEC服务器宣布的单价, AP通过解决一个凸优化问题来计算MD从MEC服务器购买的资源数量. 然后, MEC服务器计算其交易记录, 并根据多智能体近端策略优化(multi-agent proximal policy optimization, MAPPO)算法迭代调整其定价策略. 仿真结果表明, MAPPO在收益和福利方面优于许多先进的深度强化学习算法.     

CPS-enabled and knowledge-aided demand response strategy for sustainable manufacturing

The utilization of advanced industrial informatics, such as industrial internet of things and cyber-physical system (CPS), provides enhanced situation awareness and resource controllability, which are essential for flexible real-time production scheduling and control (SC). Regardless of the belief that applying these advanced technologies under electricity demand response can help alleviate electricity demand–supply mismatches and eventually improve manufacturing sustainability, significant barriers have to be overcome first. Particularly, most existing real-time SC strategies remain limited to short-term scheduling and are unsuitable for finding the optimal schedule under demand response scheme, where a long-term production scheduling is often required to determine the energy consumption shift from peak to off-peak hours. Moreover, SC strategies ensuring the desired production throughput under dynamic electricity pricing and uncertainties in manufacturing environment are largely lacking. In this research, a knowledge-aided real-time demand response strategy for CPS-enabled manufacturing systems is proposed to address the above challenges. A knowledge-aided analytical model is first applied to generate a long-term production schedule to aid the real-time control under demand response. In addition, a real-time optimization model is developed to reduce electricity costs for CPS-enabled manufacturing systems under uncertainties. The effectiveness of the proposed strategy is validated through the case study on a steel powder manufacturing system. The results indicate the exceptional performance of the proposed strategy as compared to other real-time SC strategies, leading to a reduction of electricity cost up to 35.6% without sacrificing the production throughput.     

基于DEB-ABC算法的电动汽车充电优化调度模型

随着电动汽车保有量不断上升, 其相关配套设施也面临巨大挑战, 不合理的充电资源分配在充电高峰期会造成部分充电站过度拥挤, 并且影响电网稳定运行. 提出一种考虑多目标优化的调度模型, 通过分析充电站内不同充电选项的排队时间, 并根据排队率和分时电价提出一种动态定价模型, 影响车主充电行为, 结合动态定价模型与充电需求计算充电成本, 考虑基于起讫点的充电总路径行驶时间, 以总成本最少为优化目标, 基于DEB-ABC算法进行求解. 在某区域内对1 500辆电动汽车进行仿真验证, 结果表明提出的优化调度模型可减少充电等待时间、充电成本和总行驶时间, 提高区域内充电站利用率.     

Residential Energy Scheduling for Variable Weather Solar Energy Based on Adaptive Dynamic Programming

The residential energy scheduling of solar energy is an important research area of smart grid. On the demand side, factors such as household loads, storage batteries, the outside public utility grid and renewable energy resources, are combined together as a nonlinear, time-varying, indefinite and complex system, which is difficult to manage or optimize. Many nations have already applied the residential real-time pricing to balance the burden on their grid. In order to enhance electricity efficiency of the residential micro grid, this paper presents an action dependent heuristic dynamic programming (ADHDP) method to solve the residential energy scheduling problem. The highlights of this paper are listed below. First, the weather-type classification is adopted to establish three types of programming models based on the features of the solar energy. In addition, the priorities of different energy resources are set to reduce the loss of electrical energy transmissions. Second, three ADHDP-based neural networks, which can update themselves during applications, are designed to manage the flows of electricity. Third, simulation results show that the proposed scheduling method has effectively reduced the total electricity cost and improved load balancing process. The comparison with the particle swarm optimization algorithm further proves that the present method has a promising effect on energy management to save cost.      

基于近端策略优化算法含碳捕集的综合能源系统低碳经济调度

为了实现园区综合能源系统（PIES）的低碳化经济运行和多能源互补,解决碳捕集装置耗电与捕碳需求之间的矛盾,以及不确定性源荷实时响应的问题,提出了基于近端策略优化算法含碳捕集的综合能源系统低碳经济调度方法。该方法通过在PIES中添加碳捕集装置,解决了碳捕集装置耗电和捕碳需求之间的矛盾,进而实现了PIES的低碳化运行;通过采用近端策略优化算法对PIES进行动态调度,解决了源荷的不确定性,平衡了各种能源的供给需求,进而降低了系统的运行成本。实验结果表明：该方法实现了不确定性源荷的实时响应,并相比于DDPG（deep deterministic policy gradient）和DQN（deep Q network）方法在低碳化经济运行方面具有有效性及先进性。     

A security and cost aware scheduling algorithm for heterogeneous tasks of scientific workflow in clouds

Security is increasingly critical for various scientific workflows that are big data applications and typically take quite amount of time being executed on large-scale distributed infrastructures. Cloud computing platform is such an infrastructure that can enable dynamic resource scaling on demand. Nevertheless, based on pay-per-use and hourly-based pricing model, users should pay attention to the cost incurred by renting virtual machines (VMs) from cloud data centers. Meanwhile, workflow tasks are generally heterogeneous and require different instance series (i.e., computing optimized, memory optimized, storage optimized, etc.). In this paper, we propose a security and cost aware scheduling (SCAS) algorithm for heterogeneous tasks of scientific workflow in clouds. Our proposed algorithm is based on the meta-heuristic optimization technique, particle swarm optimization (PSO), the coding strategy of which is devised to minimize the total workflow execution cost while meeting the deadline and risk rate constraints. Extensive experiments using three real-world scientific workflow applications, as well as CloudSim simulation framework, demonstrate the effectiveness and practicality of our algorithm.     

智能电网实时电价研究综述:模型与优化方法

在智能电网中,实时电价(real-time pricing,RTP)是解决智能电网供需平衡的理想需求响应机制,具有节能环保、保障用户和电能提供者最大化效益等方面的优势.在分析实时电价机制优化模型国内外发展现状基础上,总结出了实时电价优化模型:从用户的总需求量水平出发建立优化模型.相应地,总结了解决这一模型的优化方法:对偶法、内点法、分布式迭代法(分步式法)以及博弈论等优化求解方法.最后,展望了智能电网中实时电价机制的进一步研究方向.     

New optimization method based on energy management in microgrids based on energy storage systems and combined heat and power

Microgrids can be assumed as a solution model for green energy sources, energy storage systems, and combined heat and power (CHP) systems. In this work, the cost and emission minimization based on a demand response (DR) program is considered an optimization problem. To solve the mentioned problem a new multiobjective optimization algorithm (improved particle swarm optimization) is proposed based on a fuzzy mechanism to select the optimal value. The microgrid system includes two CHP units, fuel cell and battery systems, and the heat buffer tank. In this problem, two different feasible operating regions have been assumed in CHPs. Accordingly, to decrease the operational cost, time-of-use, and real-time pricing DR programs have been simulated, and the impacts of the mentioned models are evaluated overload profiles. The effectiveness of proposed models has been applied on different cases studies by different scenarios. The proposed model solved the DR program, time of use-DR and real-time pricing-DR problems. The proposed model could reduce the cost about 10%.     

移动自组网络入侵检测中主动学习算法的应用

由于媒介开放、动态拓扑、交互及资源有限等特点,移动自组网络比传统网络更需要安全保障。介绍了一种集成入侵检测模型。在该模型中带监督异常检测的分类器基于支持向量机。同时,介绍了三种应用在该模型的基于池的主动学习算法。通过与传统的自学习算法比较,显示基于池的主动学习算法能有效地减少对训练样本的依赖,同时减少噪音数据对入侵检测系统性能的影响,适用于移动自组网络对于入侵检测系统高检测率、高抗噪能力和低计算延迟的要求。     

基于需求响应的家庭节电省电优化策略研究

电力需求的日益增长给电网发电及输电环节带来了巨大挑战,配用电网负荷率的增加也给电力系统正常运行带来了安全隐患.运用智能电力需求响应技术,有效整合用户侧电网响应潜力以提升电网运行的安全性、稳定性和经济性值得深入研究.基于智能用电双向交互技术,在满足用户用电要求的基础上,最大限度满足用户舒适度及电网调峰需求为目标,提出了居民侧负荷参与电力需求响应的家庭用电优化方案,重点提出了节电省电控制策略,有负荷转移和负荷调控两种方法,可为家庭提供系统的、全面的省电措施.结合提出的控制策略,基于Matlab平台进行具体仿真,通过波形和数据的分析,详细阐述了需求响应控制策略的要点和价值,也为以后的技术发展提供了数据信息.     

EMPSO-based optimization for inter-temporal multi-product revenue management under salvage consideration

The retail market is governed by customer behavior, demand pattern and inventory replenishment policies. It is also observed that any decision would prove to be full of errors, and objective of enhancing the market share could not be achieved, without inclusion of these factors and policies. While an extensive set of literature exists on single and multi-product dynamic pricing, the issue of liquidation of leftover inventory has so far received scant attention from the researchers of Operations Management community. The current work primarily tries to bridge this research gap by addressing dual objectives of revenue maximization and reduction of salvaging losses. In this paper an inter-temporal dynamic pricing model for multiple products is developed under a market setup with price-sensitive demand. Ideas proposed by [1] and [2] have been taken into account for constructing a revenue structure. The formulated objective function is found to be tractable for deriving prices and procurement quantities of large product portfolios. A multi-objective problem has been devised to handle the optimization of normal and clearance revenue by satisfying several pragmatic constraints. Subsequently, an effective algorithm deriving its traits from Particle Swarm Optimization has been proposed to address this problem. An illustrative example from retail apparel industry has been simulated and solved by the afore-mentioned approach. To validate the model statistical analysis has been carried out and the managerial insights portrayed to reveal the practical complexities involved.     

A Novel Stackelberg-Game-Based Energy Storage Sharing Scheme Under Demand Charge

Demand response(DR) using shared energy storage systems(ESSs) is an appealing method to save electricity bills for users under demand charge and time-of-use(TOU) price. A novel Stackelberg-game-based ESS sharing scheme is proposed and analyzed in this study. In this scheme, the interactions between selfish users and an operator are characterized as a Stackelberg game. Operator holds a large-scale ESS that is shared among users in the form of energy transactions. It sells energy to users and sets...     

多级压缩空气储能系统变工况的优化运行控制

多级压缩空气储能系统以压缩空气储能及热存储技术为基础,具备冷、热、电多种能量存储及供给能力.针对多变复杂工况下系统热能合理分配问题,提出了一种多级压缩空气储能系统变工况优化运行控制策略.首先基于热力学方法分析,建立压缩空气储能系统模块化数学模型.其次根据系统负荷需求,在"以电定热"及"以热定电"两种工作模式下,分别以储热热量消耗量最小及输出电功最大为优化目标建立优化模型,求解得到不同工况下系统各部件运行参数,并以1 MW多级压缩空气储能系统为例,进行优化求解.该控制策略有效地解决了多级压缩空气储能系统在变工况下的内部能量调配、系统内部运行参数选取的问题,为实现系统高效运行提供有效途径.     

Optimal electricity rate structures for peak demand reduction using economic model predictive control

Economic model predictive control (EMPC) has recently gained popularity for managing energy consumption in buildings that are exposed to non-constant electricity prices, such as time-of-use prices or real-time prices. These electricity prices are employed directly in the objective function of the EMPC problem. This paper considers how electricity prices can be designed in order to achieve a specific objective, which in this case is minimizing peak electricity demand. A primal-dual formulation of the EMPC problem is presented that is used to determine optimal prices that minimize peak demand. The method is demonstrated on a simulated community of 900 residential homes to create a pricing structure that minimizes the peak demand of the community of homes. The pricing structure shows that homes should be given a 1-h peak demand duration, and that the peak prices given to the homes should be spread unevenly across 6 h of the afternoon.     

Residential virtual power plant with photovoltaic output forecasting and demand response

Decreasing conventional power supply is promoting the development of the distributed renewable energy sources, such as solar power and wind power. Recently rooftop photovoltaic has been widely applied, and accordingly efficient energy management is getting increasingly important for fully use of renewable energy and the peak shaving of the main grid. This paper investigates the residential energy management as a small‐scale virtual power plant (VPP) connected to the main grid includes distributed energy resources, energy storages and residential loads. The self‐organizing map (SOM) and the radical basis function (RBF) networks are adopted to classify the weather types and predict hourly photovoltaic output precisely. In a time‐of‐use electricity market, price‐based demand response is applied to adjust the demand. The residential VPP has two goals: maximum profit by selling surplus power to grid and minimum power purchased from grid. The two goals are integrated as an optimization object by introducing a weight parameter. The algorithm combining receding horizon optimization and linear programming is proposed to solve the optimization problem in residential VPP. Numerical simulation tests can help to find the most suitable value of the weight parameter. Different scenarios are simulated and discussed to demonstrate the performance of the VPP and the proposed algorithm.     

基于接纳控制的智能电网需求响应

采用效用函数刻画用户的用电满意度, 将需求响应问题建模为一类凸优化问题. 针对电力供应商的供电量不能满足用户最小用电需求的问题, 结合分布式用电量调度和实定价, 设计两类接纳控制算法. 仿真结果表明, 通过接纳控制, 满足了购电用户的最小用电需求, 保证了用户的用电质量, 能够实现电网的供需平衡.     

基于协作频谱感知的智能电网通信网性能研究

将认知无线电频谱感知技术应用于智能电网的通信网中,可以有效提高频谱资源的利用率。现有研究仅考虑单用户单供电商,但是对需求响应管理性能与感知能耗权衡问题却没有给出理想的解决方案。建立基于多节点协作频谱感知的多用户单供电商智能电网通信网模型。在此基础上,为求解该模型需求响应管理和能耗感知性能权衡问题,提出基于多目标粒子群(MOPSO)的求解方法。仿真结果表明,所提协作频谱感知模型可以显著提高系统需求响应管理性能;MOPSO算法可实现系统需求响应管理性能和感知能耗的最佳权衡,有利于决策者根据实际要求灵活选择最优方案。